Rule-based data stream processing

ABSTRACT

Systems and methods for rule-based data stream processing by data collection, indexing, and visualization systems. An example method includes: receiving, by the computer system, an input data stream comprising raw machine data; processing the raw machine data by a data processing pipeline that produces transformed machine data, wherein the data processing pipeline comprises an ordered plurality of pipeline stages, wherein a pipeline stage of the ordered plurality of pipeline stages applies a rule of a set of rules to an input of the pipeline stage, wherein the rule specifies an action to be performed on the input of the pipeline stage responsive to evaluating a conditional expression applied to the input of the pipeline stage, wherein the action generates an output of the pipeline stage, and wherein the rule is selected based on a source type associated with the input data stream; and supplying the transformed machine data to a data collection, indexing, and visualization system.

TECHNICAL FIELD

Embodiments of the present disclosure are generally related to datacollection, indexing, and visualization systems, and more specifically,to rule-based data stream processing.

BACKGROUND

A data collection, indexing, and visualization system may providereal-time operational intelligence that enables organizations tocollect, index, and search machine data from various websites,applications, servers, networks, and mobile devices that power theirbusinesses. The data collection, indexing, and visualization system maybe particularly useful for analyzing data which is commonly found insystem log files, network data, and other data input sources.

BRIEF DESCRIPTION OF THE DRAWINGS

The examples described herein will be understood more fully from thedetailed description given below and from the accompanying drawings,which, however, should not be taken to limit the application to thespecific examples, but are for explanation and understanding.

FIG. 1 is a block diagram of an example networked computer environment,in accordance with example embodiments;

FIG. 2 is a block diagram of an example data collection, indexing, andvisualization system, in accordance with example embodiments;

FIG. 3 is a block diagram of an example cloud-based data collection,indexing, and visualization system, in accordance with exampleembodiments;

FIG. 4 is a block diagram of an example data collection, indexing, andvisualization system that performs searches across external datasystems, in accordance with example embodiments;

FIG. 5A is a flowchart of an example method that illustrates howindexers process, index, and store data received from forwarders, inaccordance with example embodiments;

FIG. 5B is a block diagram of a data structure in which time-stampedevent data may be stored in a data store, in accordance with exampleembodiments;

FIG. 5C provides a visual representation of the manner in which apipelined search language or query operates, in accordance with exampleembodiments;

FIG. 6A is a flow diagram of an example method that illustrates how asearch head and indexers perform a search query, in accordance withexample embodiments;

FIG. 6B provides a visual representation of an example manner in which apipelined command language or query operates, in accordance with exampleembodiments;

FIG. 7A is a diagram of an example scenario where a common customeridentifier is found among log data received from three disparate datasources, in accordance with example embodiments;

FIG. 7B illustrates an example of processing keyword searches and fieldsearches, in accordance with disclosed embodiments;

FIG. 7C illustrates an example of creating and using an inverted index,in accordance with example embodiments;

FIG. 7D depicts a flowchart of example use of an inverted index in apipelined search query, in accordance with example embodiments;

FIG. 8A is an interface diagram of an example user interface for asearch screen, in accordance with example embodiments;

FIG. 8B is an interface diagram of an example user interface for a datasummary dialog that enables a user to select various data sources, inaccordance with example embodiments;

FIGS. 9, 10, 11A, 11B, 11C, 11D, 12, 13, 14, and 15 are interfacediagrams of example report generation user interfaces, in accordancewith example embodiments;

FIG. 16 is an example search query received from a client and executedby search peers, in accordance with example embodiments;

FIG. 17A is an interface diagram of an example user interface of a keyindicators view, in accordance with example embodiments;

FIG. 17B is an interface diagram of an example user interface of anincident review dashboard, in accordance with example embodiments;

FIG. 17C is a tree diagram of an example a proactive monitoring tree, inaccordance with example embodiments;

FIG. 17D is an interface diagram of an example a user interfacedisplaying both log data and performance data, in accordance withexample embodiments;

FIG. 18 schematically illustrates an example data stream processingarchitecture implemented by data collection, indexing, and visualizationsystems operating in accordance with aspects of the present disclosure.

FIG. 19 schematically illustrates an example data processing pipelineimplemented by the data stream processing operating in accordance withaspects of the present disclosure.

FIG. 20 schematically illustrates an example structure of a rulesetemployed by the data stream processing for implementing a dataprocessing pipeline in accordance with aspects of the presentdisclosure.

FIG. 21 is a flow diagram of an embodiment of a method of rule-baseddata stream processing implemented by a data stream processor operatingin accordance with aspects of the present disclosure.

FIG. 22 is a flow diagram of an embodiment of a method of implementing apipeline stage by a data stream processor operating in accordance withaspects of the present disclosure.

DETAILED DESCRIPTION

Described herein are systems and methods for rule-based data streamprocessing by data collection, indexing, and visualization system. Anincoming data stream may comprise raw machine data, which may reflectactivity of an external system (e.g., as an information technology (IT)environment), and may be produced by one or more components of theexternal system. The raw machine data may comprise a sequence of datapoints. In an illustrative example, the data points may reflectperformance measurements of an external system or environment that areassociated with successive points in time. The data points may representevents, i.e., changes of the state of the external system orenvironment.

In some instances, the raw machine data may adhere to a predefinedformat, e.g., where data items with specific data formats are stored atpredefined locations in the data. In other instances, the raw machinedata may include repeatable patterns and/or field and record separators.For example, one or more lines from an operating system log may reflectdifferent types of performance and diagnostic information associatedwith a specific point in time.

A data collection, indexing, and visualization system operating inaccordance with aspects of the present disclosure may employ one or moredata ingestion components (e.g., forwarders, connectors, ingestservices, collect services, REST APIs, etc.) in order to ingest rawmachine data from one or more sources. The ingested data stream may beprocessed by a data stream processor implementing a data processingpipeline, which may transform the data stream and feed the transformeddata stream to one or more indexers of the data collection, indexing,and visualization system and/or to one or more third party systems forfurther processing.

The data stream processor may implement a data processing pipeline thatperforms a series of data processing operations (“pipeline stages”),which define transformations of data flowing from one or more datasources to one or more data sinks. The data processing operationsperformed by the data processing pipeline may include data aggregation(aggregating data based on specific conditions), data formatting(formatting data based on specified conditions), data routing(delivering data to one or more destinations based on specifiedconditions), data enrichment (enriching the data with additional fieldsbased on lookups, etc.), and/or data filtering (filtering or routingnoisy data to one or more destinations based on specified conditions).

The data sources feeding the data to the data processing pipeline may beassociated with various external systems and environments, each of whichmay comprise, for example, disparate computer systems running a widevariety of software applications and middleware on one or moreplatforms. Accordingly, processing machine data that is generated bythese computer systems may require employment of multiple source datatype-specific pipelines and/or pipeline stages (e.g., vendor-specific,platform specific, and/or technology-specific). However, creating andmaintaining multiple versions of data processing pipelines and/orpipeline stages may significantly increase the development andproduction costs associated with the data collection, indexing, andvisualization system.

The systems and methods of the present disclosure overcome theabove-noted challenges by employing configurable rules for performingdata processing operations associated with respective data processingpipeline stages. The data stream processor operating in accordance withaspects of the present disclosure may employ one or more sets of rulesfor implementing the data processing pipeline. Each set of rules mayinclude one or more rules, and each rule may specify an action to beperformed responsive to successfully matching a specified pattern to oneor more points of the input data stream that is fed to the pipelinestage. The action performed by the rule may be specified by the “kind”rule attribute, which may be specified for one or more rules that aregrouped together into a set of rules. In some implementations, theactions that may be invoked by a given rule may be restricted based onthe value of the “kind” attribute of the rule, thus implementingsecurity and/or performance constraints.

Furthermore, each rule may be characterized by the “source type”attribute, which may specify the source type to be matched to the inputdata stream. The source type of the input data stream may reflectvendor, platform, and/or technology associated with the externalsystem(s) that generated the input data stream. The source type may bedetermined by applying, to one or more data points of the input datastream, special rules that implement keyword matching or patternmatching in order to associate the input data stream with a particularsource type. In some implementations, the special rule may employ one ormore trainable classifiers (e.g., implemented by neural networks). Atrainable classifier may receive one or more data points of the inputdata stream and may yield a degree of association of the input datastream with a certain source type.

As noted herein above, a function call within the data processingpipeline may specify the kind of rule to be applied to the input datastream. Accordingly, executing the function call would involveidentifying, among the rules of the specified kind, one or more ruleswhose “source type” attribute matches the source type of the input datastream. The identified rules for the “source type” may be cached toimprove the performance of the subsequent function calls. The result ofapplying, to the matching data points, the actions specified by therules forms the output data stream of the pipeline stage.

The rules utilized by the data processing pipeline may be grouped intoone or more rulesets, and may be grouped by the source type attributeand/or by the kind attribute. In various illustrative examples, arule-based pipeline stage may perform line breaking, timestampextraction, field extraction, source type setting and/or various otherdata transformations, by applying the rules corresponding to the sourcetypes.

Employing the source type-specific rules allows utilizing a single dataprocessing pipeline for processing data from multiple different datasources, rather than maintaining multiple data processing pipelines ormultiple branches in a single data processing pipeline. Furthermore,adding a new data source would not require any modifications of the dataprocessing pipeline, and may be accomplished by adding a set of rulesthat are designed to perform the requisite pipeline stage functions onthe data streams associated with the newly added data source. Similarly,modifying or removing any data source may be accomplished by modifyingor removing the corresponding set of rules.

The transformed data stream produced by the data processing pipeline maybe fed to one or more indexers and/or other components of the datacollection, indexing, and visualization system, as described in moredetail herein below.

In an illustrative example, the data collection, indexing, andvisualization system may be represented by SPLUNK® ENTERPRISE systemdeveloped by Splunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISEsystem is the leading platform for providing real-time operationalintelligence that enables organizations to collect, index, and searchmachine data from various websites, applications, servers, networks, andmobile devices that power their businesses. The data collection,indexing, and visualization system is particularly useful for analyzingdata which is commonly found in system log files, network data, andother data input sources. Although many of the techniques describedherein are explained with reference to a data collection, indexing, andvisualization system similar to the SPLUNK® ENTERPRISE system, thesetechniques are also applicable to other types of data systems.

As noted herein above, in the data collection, indexing, andvisualization system, machine data items are collected and stored as“events”. An event comprises a portion of machine data and is associatedwith a specific point in time. The portion of machine data may reflectactivity in an IT environment and may be produced by a component of thatIT environment, where the events may be searched to provide insight intothe IT environment, thereby improving the performance of components inthe IT environment. Events may be derived from “time series data,” wherethe time series data comprises a sequence of data points (e.g.,performance measurements from a computer system, etc.) that areassociated with successive points in time. In general, each event has aportion of machine data that is associated with a timestamp that isderived from the portion of machine data in the event. A timestamp of anevent may be determined through interpolation between temporallyproximate events having known timestamps or may be determined based onother configurable rules for associating timestamps with events.

In some instances, machine data may have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data associated withfields in a database table. In other instances, machine data may nothave a predefined format (e.g., may not be at fixed, predefinedlocations), but may have repeatable (e.g., non-random) patterns. Thismeans that some machine data may comprise various data items ofdifferent data types that may be stored at different locations withinthe data. For example, when the data source is an operating system log,an event may include one or more lines from the operating system logcontaining machine data that includes different types of performance anddiagnostic information associated with a specific point in time (e.g., atimestamp).

Examples of components which may generate machine data from which eventsmay be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, distributed ledger nodes, etc. Themachine data generated by such data sources may include, for example andwithout limitation, server log files, activity log files, configurationfiles, messages, network packet data, performance measurements, sensormeasurements, distributed ledger transactions, etc.

The data collection, indexing, and visualization system uses a flexibleschema to specify how to extract information from events. A flexibleschema may be developed and redefined as needed. Note that a flexibleschema may be applied to events “on the fly,” when it is needed (e.g.,at search time, index time, ingestion time, etc.). When the schema isnot applied to events until search time, the schema may be referred toas a “late-binding schema.”

During operation, the data collection, indexing, and visualizationsystem receives machine data from any type and number of sources (e.g.,one or more system logs, streams of network packet data, sensor data,application program data, error logs, stack traces, system performancedata, etc.). The system parses the machine data to produce events eachhaving a portion of machine data associated with a timestamp. The systemstores the events in a data store. The system enables users to runqueries against the stored events to, for example, retrieve events thatmeet criteria specified in a query, such as criteria indicating certainkeywords or having specific values in defined fields. As used herein,the term “field” refers to a location in the machine data of an eventcontaining one or more values for a specific data item. A field may bereferenced by a field name associated with the field. As will bedescribed in more detail herein, a field is defined by an extractionrule (e.g., a regular expression) that derives one or more values or asub-portion of text from the portion of machine data in each event toproduce a value for the field for that event. The set of values producedare semantically-related (such as IP address), even though the machinedata in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As described above, the system stores the events in a data store. Theevents stored in the data store are field-searchable, wherefield-searchable herein refers to the ability to search the machine data(e.g., the raw machine data) of an event based on a field specified insearch criteria. For example, a search having criteria that specifies afield name “UserID” may cause the system to field-search the machinedata of events to identify events that have the field name “UserID.” Inanother example, a search having criteria that specifies a field name“UserID” with a corresponding field value “12345” may cause the systemto field-search the machine data of events to identify events havingthat field-value pair (e.g., field name “UserID” with a correspondingfield value of “12345”). Events are field-searchable using one or moreconfiguration files associated with the events. Each configuration fileincludes one or more field names, where each field name is associatedwith a corresponding extraction rule and a set of events to which thatextraction rule applies. The set of events to which an extraction ruleapplies may be identified by metadata associated with the set of events.For example, an extraction rule may apply to a set of events that areeach associated with a particular host, source, or source type. Whenevents are to be searched based on a particular field name specified ina search, the system uses one or more configuration files to determinewhether there is an extraction rule for that particular field name thatapplies to each event that falls within the criteria of the search. Ifso, the event is considered as part of the search results (andadditional processing may be performed on that event based on criteriaspecified in the search). If not, the next event is similarly analyzed,and so on.

As noted above, the data collection, indexing, and visualization systemutilizes a late-binding schema while performing queries on events. Oneaspect of a late-binding schema is applying extraction rules to eventsto extract values for specific fields during search time. Morespecifically, the extraction rule for a field may include one or moreinstructions that specify how to extract a value for the field from anevent. An extraction rule may generally include any type of instructionfor extracting values from events. In some cases, an extraction rulecomprises a regular expression, where a sequence of characters forms asearch pattern. An extraction rule comprising a regular expression isreferred to herein as a regex rule. The system applies a regex rule toan event to extract values for a field associated with the regex rule,where the values are extracted by searching the event for the sequenceof characters defined in the regex rule.

In the data collection, indexing, and visualization system, a fieldextractor may be configured to automatically generate extraction rulesfor certain fields in the events when the events are being created,indexed, or stored, or possibly at a later time. Alternatively, a usermay manually define extraction rules for fields using a variety oftechniques. In contrast to a conventional schema for a database system,a late-binding schema is not defined at data ingestion time. Instead,the late-binding schema may be developed on an ongoing basis until thetime a query is actually executed. This means that extraction rules forthe fields specified in a query may be provided in the query itself, ormay be located during execution of the query. Hence, as a user learnsmore about the data in the events, the user may continue to refine thelate-binding schema by adding new fields, deleting fields, or modifyingthe field extraction rules for use the next time the schema is used bythe system. Because the data collection, indexing, and visualizationsystem maintains the underlying machine data and uses a late-bindingschema for searching the machine data, it enables a user to continueinvestigating and learn valuable insights about the machine data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent and/or similar data items, even thoughthe fields may be associated with different types of events thatpossibly have different data formats and different extraction rules. Byenabling a common field name to be used to identify equivalent and/orsimilar fields from different types of events generated by disparatedata sources, the system facilitates use of a “common information model”(CIM) across the disparate data sources (further discussed with respectto FIG. 7A).

FIG. 1 is a block diagram of an example networked computer environment100, in accordance with example embodiments. Those skilled in the artwould understand that FIG. 1 represents one example of a networkedcomputer system and other embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In some embodiments, one or more client devices 102 are coupled to oneor more host devices 106 and a data collection, indexing, andvisualization system 108 via one or more networks 104. Networks 104broadly represent one or more LANs, WANs, cellular networks (e.g., LTE,HSPA, 3G, and other cellular technologies), and/or networks using any ofwired, wireless, terrestrial microwave, or satellite links, and mayinclude the public Internet.

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types of machine data.For example, a host application 114 comprising a web server may generateone or more web server logs in which details of interactions between theweb server and any number of client devices 102 is recorded. As anotherexample, a host device 106 comprising a router may generate one or morerouter logs that record information related to network traffic managedby the router. As yet another example, a host application 114 comprisinga database server may generate one or more logs that record informationrelated to requests sent from other host applications 114 (e.g., webservers or application servers) for data managed by the database server.

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 may provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

In some embodiments, each client device 102 may host or execute one ormore client applications 110 that are capable of interacting with one ormore host devices 106 via one or more networks 104. For instance, aclient application 110 may be or comprise a web browser that a user mayuse to navigate to one or more web sites or other resources provided byone or more host devices 106. As another example, a client application110 may comprise a mobile application or “app.” For example, an operatorof a network-based service hosted by one or more host devices 106 maymake available one or more mobile apps that enable users of clientdevices 102 to access various resources of the network-based service. Asyet another example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In some embodiments, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In some embodiments, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that may be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality may become part of the application itself.

In some embodiments, an SDK or other code for implementing themonitoring functionality may be offered by a provider of a datacollection, indexing, and visualization system, such as a system 108. Insuch cases, the provider of the system 108 may implement the custom codeso that performance data generated by the monitoring functionality issent to the system 108 to facilitate analysis of the performance data bya developer of the client application or other users.

In some embodiments, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 may add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In some embodiments, the monitoring component 112 may monitor one ormore aspects of network traffic sent and/or received by a clientapplication 110. For example, the monitoring component 112 may beconfigured to monitor data packets transmitted to and/or from one ormore host applications 114. Incoming and/or outgoing data packets may beread or examined to identify network data contained within the packets,for example, and other aspects of data packets may be analyzed todetermine a number of network performance statistics. Monitoring networktraffic may enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In some embodiments, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data may be transmitted to a datacollection, indexing, and visualization system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 may be distributed to clientdevices 102. Applications generally may be distributed to client devices102 in any manner, or they may be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In some embodiments, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In some embodiments, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“network Latency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

FIG. 2 is a block diagram of an example data collection, indexing, andvisualization system 108, in accordance with example embodiments. System108 includes one or more data stream processors 204 that receive datafrom a variety of input data sources 202, and one or more indexers 206that process and store the data in one or more data stores 208. Thesedata stream processors 204 and indexers 206 may comprise separatecomputer systems, or may alternatively comprise separate processesexecuting on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatmay be consumed by system 108. Examples of a data sources 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the data stream processors 204 identify which indexers206 receive data collected from a data source 202 and forward the datato the appropriate indexers. Data stream processors 204 may also performoperations on the data before forwarding, including removing extraneousdata, detecting timestamps in the data, parsing data, indexing data,routing data based on criteria relating to the data being routed, and/orperforming other data transformations.

In some embodiments, a data stream processor 204 may comprise a serviceaccessible to client devices 102 and host devices 106 via a network 104.For example, one type of data stream processor 204 may be capable ofconsuming vast amounts of real-time data from a potentially large numberof client devices 102 and/or host devices 106. The data stream processor204 may, for example, comprise a computing device which implementsmultiple data processing pipelines or “queues” to handle forwarding ofnetwork data to indexers 206, as described in more detail herein belowwith references to FIGS. 18-22. A data stream processor 204 may alsoperform many of the functions that are performed by an indexer. Forexample, a data stream processor 204 may perform keyword extractions onraw data or parse raw data to create events. A data stream processor 204may generate time stamps for events. Additionally, or alternatively, adata stream processor 204 may perform routing of events to indexers 206.Data store 208 may contain events derived from machine data from avariety of sources all pertaining to the same component in an ITenvironment, and this data may be produced by the machine in question orby other components in the IT environment.

The example data collection, indexing, and visualization system 108described in reference to FIG. 2 comprises several system components,including one or more forwarders, indexers, and search heads. In someenvironments, a user of a data collection, indexing, and visualizationsystem 108 may install and configure, on computing devices owned andoperated by the user, one or more software applications that implementsome or all of these system components. For example, a user may installa software application on server computers owned by the user andconfigure each server to operate as one or more of a forwarder, anindexer, a search head, etc. This arrangement generally may be referredto as an “on-premises” solution. That is, the system 108 is installedand operates on computing devices directly controlled by the user of thesystem. Some users may prefer an on-premises solution because it mayprovide a greater level of control over the configuration of certainaspects of the system (e.g., security, privacy, standards, controls,etc.). However, other users may instead prefer an arrangement in whichthe user is not directly responsible for providing and managing thecomputing devices upon which various components of system 108 operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a datacollection, indexing, and visualization system instead may be providedas a cloud-based service. In this context, a cloud-based service refersto a service hosted by one more computing resources that are accessibleto end users over a network, for example, by using a web browser orother application on a client device to interface with the remotecomputing resources. For example, a service provider may provide acloud-based data collection, indexing, and visualization system bymanaging computing resources configured to implement various aspects ofthe system (e.g., forwarders, indexers, search heads, etc.) and byproviding access to the system to end users via a network. Typically, auser may pay a subscription or other fee to use such a service. Eachsubscribing user of the cloud-based service may be provided with anaccount that enables the user to configure a customized cloud-basedsystem based on the user's preferences.

FIG. 3 illustrates a block diagram of an example cloud-based datacollection, indexing, and visualization system. Similar to the system ofFIG. 2, the networked computer system 300 includes input data sources202 and data stream processors 204. These input data sources andforwarders may be in a subscriber's private computing environment.Alternatively, they might be directly managed by the service provider aspart of the cloud service. In the example system 300, one or more datastream processors 204 and client devices 302 are coupled to acloud-based data collection, indexing, and visualization system 306 viaone or more networks 304. Network 304 broadly represents one or moreLANs, WANs, cellular networks, intranetworks, internetworks, etc., usingany of wired, wireless, terrestrial microwave, satellite links, etc.,and may include the public Internet, and is used by client devices 302and data stream processors 204 to access the system 306. Similar to thesystem of 38, each of the data stream processors 204 may be configuredto receive data from an input source and to forward the data to othercomponents of the system 306 for further processing.

In some embodiments, a cloud-based data collection, indexing, andvisualization system 306 may comprise a plurality of system instances308. In general, each system instance 308 may include one or morecomputing resources managed by a provider of the cloud-based system 306made available to a particular subscriber. The computing resourcescomprising a system instance 308 may, for example, include one or moreservers or other devices configured to implement one or more forwarders,indexers, search heads, and other components of a data collection,indexing, and visualization system, similar to system 108. As indicatedabove, a subscriber may use a web browser or other application of aclient device 302 to access a web portal or other interface that enablesthe subscriber to configure an instance 308.

Providing a data collection, indexing, and visualization system asdescribed in reference to system 108 as a cloud-based service presents anumber of challenges. Each of the components of a system 108 (e.g.,forwarders, indexers, and search heads) may at times refer to variousconfiguration files stored locally at each component. Theseconfiguration files typically may involve some level of userconfiguration to accommodate particular types of data a user desires toanalyze and to account for other user preferences. However, in acloud-based service context, users typically may not have direct accessto the underlying computing resources implementing the various systemcomponents (e.g., the computing resources comprising each systeminstance 308) and may desire to make such configurations indirectly, forexample, using one or more web-based interfaces. Thus, the techniquesand systems described herein for providing user interfaces that enable auser to configure source type definitions are applicable to bothon-premises and cloud-based service contexts, or some combinationthereof (e.g., a hybrid system where both an on-premises environment,such as SPLUNK® ENTERPRISE, and a cloud-based environment, such asSPLUNK CLOUD™, are centrally visible).

FIG. 4 shows a block diagram of an example of a data collection,indexing, and visualization system 108 that provides transparent searchfacilities for data systems that are external to the data collection,indexing, and visualization system. Such facilities are available in theSplunk® Analytics for Hadoop® system provided by Splunk Inc. of SanFrancisco, Calif. Splunk® Analytics for Hadoop® represents an analyticsplatform that enables business and IT teams to rapidly explore, analyze,and visualize data in Hadoop® and NoSQL data stores.

The search head 210 of the data collection, indexing, and visualizationsystem receives search requests from one or more client devices 404 overnetwork connections 420. As discussed above, the data collection,indexing, and visualization system 108 may reside in an enterpriselocation, in the cloud, etc. FIG. 4 illustrates that multiple clientdevices 404 a, 404 b . . . 404 n may communicate with the datacollection, indexing, and visualization system 108. The client devices404 may communicate with the data collection, indexing, andvisualization system using a variety of connections. For example, oneclient device in FIG. 4 is illustrated as communicating over an Internet(Web) protocol, another client device is illustrated as communicatingvia a command line interface, and another client device is illustratedas communicating via a software developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 404 references an index maintained by the data collection,indexing, and visualization system, then the search head 210 connects toone or more indexers 206 of the data collection, indexing, andvisualization system for the index referenced in the request parameters.That is, if the request parameters of the search request reference anindex, then the search head accesses the data in the index via theindexer. The data collection, indexing, and visualization system 108 mayinclude one or more indexers 206, depending on system access resourcesand requirements. As described further below, the indexers 206 retrievedata from their respective local data stores 208 as specified in thesearch request. The indexers and their respective data stores maycomprise one or more storage devices and typically reside on the samesystem, though they may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data collection, indexing, and visualizationsystem, then the search head 210 may access the external data collectionthrough an External Result Provider (ERP) process 410. An external datacollection may be referred to as a “virtual index” (plural, “virtualindices”). An ERP process provides an interface through which the searchhead 210 may access virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 410, 412. FIG. 4 shows two ERP processes 410, 412 that connectto respective remote (external) virtual indices, which are indicated asa Hadoop or another system 414 (e.g., Amazon S3, Amazon EMR, otherHadoop® Compatible File Systems (HCFS), etc.) and a relational databasemanagement system (RDBMS) 416. Other virtual indices may include otherfile organizations and protocols, such as Structured Query Language(SQL) and the like. The ellipses between the ERP processes 410, 412indicate optional additional ERP processes of the data collection,indexing, and visualization system 108. An ERP process may be a computerprocess that is initiated or spawned by the search head 210 and isexecuted by the search data collection, indexing, and visualizationsystem 108. Alternatively, or additionally, an ERP process may beprocess spawned by the search head 210 on the same or different hostsystem as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to a SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 410, 412 receive a search request from the search head210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 410, 412 may communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 410, 412 may be implemented as a process of the datacollection, indexing, and visualization system. Each ERP process may beprovided by the data collection, indexing, and visualization system, ormay be provided by process or application providers who are independentof the data collection, indexing, and visualization system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 410, 412 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices414, 416, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 404 may communicate with the data collection, indexing,and visualization system 108 through a network interface 420, e.g., oneor more LANs, WANs, cellular networks, intranetworks, and/orinternetworks using any of wired, wireless, terrestrial microwave,satellite links, etc., and may include the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. Pat. No. 9,514,189, entitled “PROCESSING ASYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”, issued on 6 Dec.2016, each of which is hereby incorporated by reference in its entiretyfor all purposes.

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes may operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointmay stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the machinedata obtained from the external data source) are provided to the searchhead, which may then process the results data (e.g., break the machinedata into events, timestamp it, filter it, etc.) and integrate theresults data with the results data from other external data sources,and/or from data stores of the search head. The search head performssuch processing and may immediately start returning interim (streamingmode) results to the user at the requesting client device;simultaneously, the search head is waiting for the ERP process toprocess the data it is retrieving from the external data source as aresult of the concurrently executing reporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the machined data or unprocesseddata necessary to respond to a search request) to the search head,enabling the search head to process the interim results and beginproviding to the client or search requester interim results that areresponsive to the query. Meanwhile, in this mixed mode, the ERP alsooperates concurrently in reporting mode, processing portions of machinedata in a manner responsive to the search query. Upon determining thatit has results from the reporting mode available to return to the searchhead, the ERP may halt processing in the mixed mode at that time (orsome later time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically, the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the machine data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode may involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser may request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process may operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head mayintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process may complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itmay begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results may be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults may be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return machine data tothe search head. As noted, the ERP process could be configured tooperate in streaming mode alone and return just the machine data for thesearch head to process in a way that is responsive to the searchrequest. Alternatively, the ERP process may be configured to operate inthe reporting mode only. Also, the ERP process may be configured tooperate in streaming mode and reporting mode concurrently, as described,with the ERP process stopping the transmission of streaming results tothe search head when the concurrently running reporting mode has caughtup and started providing results. The reporting mode does not requirethe processing of all machine data that is responsive to the searchquery request before the ERP process starts returning results; rather,the reporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process may be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process maybe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

FIG. 5A is a flow chart of an example method that illustrates howindexers process, index, and store data received from forwarders, inaccordance with example embodiments. The data flow illustrated in FIG.5A is provided for illustrative purposes only; those skilled in the artwould understand that one or more of the steps of the processesillustrated in FIG. 5A may be removed or that the ordering of the stepsmay be changed. Furthermore, for the purposes of illustrating a clearexample, one or more particular system components are described in thecontext of performing various operations during each of the data flowstages. For example, a forwarder is described as receiving andprocessing machine data during an input phase; an indexer is describedas parsing and indexing machine data during parsing and indexing phases;and a search head is described as performing a search query during asearch phase. However, other system arrangements and distributions ofthe processing steps across system components may be used.

At block 502, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In some embodiments, a forwarderreceives the raw data and may segment the data stream into “blocks”,possibly of a uniform data size, to facilitate subsequent processingsteps.

At block 504, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In someembodiments, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The data collection, indexing, and visualization system allowsforwarding of data from one data intake and query instance to another,or even to a third-party system. The data collection, indexing, andvisualization system may employ different types of forwarders in aconfiguration.

In some embodiments, a forwarder may contain the essential componentsneeded to forward data. A forwarder may gather data from a variety ofinputs and forward the data to an indexer for indexing and searching. Aforwarder may also tag metadata (e.g., source, source type, host, etc.).

In some embodiments, a forwarder has the capabilities of theaforementioned forwarder as well as additional capabilities. Theforwarder may parse data before forwarding the data (e.g., may associatea time stamp with a portion of data and create an event, etc.) and mayroute data based on criteria such as source or type of event. Theforwarder may also index data locally while forwarding the data toanother indexer.

At block 506, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In some embodiments,to organize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries withinthe received data that indicate the portions of machine data for events.In general, these properties may include regular expression-based rulesor delimiter rules where, for example, event boundaries may be indicatedby predefined characters or character strings. These predefinedcharacters may include punctuation marks or other special charactersincluding, for example, carriage returns, tabs, spaces, line breaks,etc. If a source type for the data is unknown to the indexer, an indexermay infer a source type for the data by examining the structure of thedata. Then, the indexer may apply an inferred source type definition tothe data to create the events.

At block 508, the indexer determines a timestamp for each event. Similarto the process for parsing machine data, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data for the event, tointerpolate time values based on timestamps associated with temporallyproximate events, to create a timestamp based on a time the portion ofmachine data was received or generated, to use the timestamp of aprevious event, or use any other rules for determining timestamps.

At block 510, the indexer associates with each event one or moremetadata fields including a field containing the timestamp determinedfor the event. In some embodiments, a timestamp may be included in themetadata fields. These metadata fields may include any number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 504, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 512, an indexer may optionally apply one or moretransformations to data included in the events created at block 506. Forexample, such transformations may include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to events may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

FIG. 5C illustrates an illustrative example of machine data may bestored in a data store in accordance with various disclosed embodiments.In other embodiments, machine data may be stored in a flat file in acorresponding bucket with an associated index file, such as a timeseries index or “TSIDX.” As such, the depiction of machine data andassociated metadata as rows and columns in the table of FIG. 5C ismerely illustrative and is not intended to limit the data format inwhich the machine data and metadata is stored in various embodimentsdescribed herein. In one particular embodiment, machine data may bestored in a compressed or encrypted formatted. In such embodiments, themachine data may be stored with or be associated with data thatdescribes the compression or encryption scheme with which the machinedata is stored. The information about the compression or encryptionscheme may be used to decompress or decrypt the machine data, and anymetadata with which it is stored, at search time.

As mentioned above, certain metadata, e.g., host 536, source 537, sourcetype 538 and timestamps 535 may be generated for each event, andassociated with a corresponding portion of machine data 539 when storingthe event data in a data store, e.g., data store 208. Any of themetadata may be extracted from the corresponding machine data, orsupplied or defined by an entity, such as a user or computer system. Themetadata fields may become part of or stored with the event. Note thatwhile the time-stamp metadata field may be extracted from the raw dataof each event, the values for the other metadata fields may bedetermined by the indexer based on information it receives pertaining tothe source of the data separate from the machine data.

While certain default or user-defined metadata fields may be extractedfrom the machine data for indexing purposes, all the machine data withinan event may be maintained in its original condition. As such, inembodiments in which the portion of machine data included in an event isunprocessed or otherwise unaltered, it is referred to herein as aportion of raw machine data. In other embodiments, the port of machinedata in an event may be processed or otherwise altered. As such, unlesscertain information needs to be removed for some reasons (e.g.,extraneous information, confidential information), all the raw machinedata contained in an event may be preserved and saved in its originalform. Accordingly, the data store in which the event records are storedis sometimes referred to as a “raw record data store.” The raw recorddata store contains a record of the raw event data tagged with thevarious default fields.

In FIG. 5C, the first three rows of the table represent events 531, 532,and 533 and are related to a server access log that records requestsfrom multiple clients processed by a server, as indicated by entry of“access.log” in the source column 536.

In the example shown in FIG. 5C, each of the events 531-533 isassociated with a discrete request made from a client device. The rawmachine data generated by the server and extracted from a server accesslog may include the IP address of the client 540, the user id of theperson requesting the document 541, the time the server finishedprocessing the request 542, the request line from the client 543, thestatus code returned by the server to the client 545, the size of theobject returned to the client (in this case, the gif file requested bythe client) 546 and the time spent to serve the request in microseconds544. As seen in FIG. 5C, all the raw machine data retrieved from theserver access log is retained and stored as part of the correspondingevents, 531-533 in the data store.

Event 534 is associated with an entry in a server error log, asindicated by “error.log” in the source column 537 that records errorsthat the server encountered when processing a client request. Similar tothe events related to the server access log, all the raw machine data inthe error log file pertaining to event 534 may be preserved and storedas part of the event 534.

Saving minimally processed or unprocessed machine data in a data storeassociated with metadata fields in the manner similar to that shown inFIG. 5C is advantageous because it allows search of all the machine dataat search time instead of searching only previously specified andidentified fields or field-value pairs. As mentioned above, because datastructures used by various embodiments of the present disclosuremaintain the underlying raw machine data and use a late-binding schemafor searching the raw machines data, it enables a user to continueinvestigating and learn valuable insights about the raw data. In otherwords, the user is not compelled to know about all the fields ofinformation that will be needed at data ingestion time. As a user learnsmore about the data in the events, the user may continue to refine thelate-binding schema by defining new extraction rules, or modifying ordeleting existing extraction rules used by the system.

At blocks 514 and 516, an indexer may optionally generate a keywordindex to facilitate fast keyword searching for events. To build akeyword index, at block 514, the indexer identifies a set of keywords ineach event. At block 516, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexermay access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries for fieldname-value pairs found in events, where a field name-value pair mayinclude a pair of keywords connected by a symbol, such as an equals signor colon. This way, events containing these field name-value pairs maybe quickly located. In some embodiments, fields may automatically begenerated for some or all of the field names of the field name-valuepairs at the time of indexing. For example, if the string“dest=10.0.1.2” is found in an event, a field named “dest” may becreated for the event, and assigned a value of “10.0.1.2”.

At block 518, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In some embodiments, the stored events are organizedinto “buckets,” where each bucket stores events associated with aspecific time range based on the timestamps associated with each event.This improves time-based searching, as well as allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events may be stored inflash memory rather than on a hard disk. In some embodiments, eachbucket may be associated with an identifier, a time range, and a sizeconstraint.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers mayanalyze events for a query in parallel. For example, using mapreducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize the data retrieval process by searchingbuckets corresponding to time ranges that are relevant to a query. Insome embodiments, each bucket may be associated with an identifier, atime range, and a size constraint. In certain embodiments, a bucket maycorrespond to a file system directory and the machine data, or events,of a bucket may be stored in one or more files of the file systemdirectory. The file system directory may include additional files, suchas one or more inverted indexes, high performance indexes, permissionsfiles, configuration files, etc.

In some embodiments, each indexer has a home directory and a colddirectory. The home directory of an indexer stores hot buckets and warmbuckets, and the cold directory of an indexer stores cold buckets. A hotbucket is a bucket that is capable of receiving and storing events. Awarm bucket is a bucket that may no longer receive events for storagebut has not yet been moved to the cold directory. A cold bucket is abucket that may no longer receive events and may be a bucket that waspreviously stored in the home directory. The home directory may bestored in faster memory, such as flash memory, as events may be activelywritten to the home directory, and the home directory may typicallystore events that are more frequently searched and thus are accessedmore frequently. The cold directory may be stored in slower and/orlarger memory, such as a hard disk, as events are no longer beingwritten to the cold directory, and the cold directory may typicallystore events that are not as frequently searched and thus are accessedless frequently. In some embodiments, an indexer may also have aquarantine bucket that contains events having potentially inaccurateinformation, such as an incorrect time stamp associated with the eventor a time stamp that appears to be an unreasonable time stamp for thecorresponding event. The quarantine bucket may have events from any timerange; as such, the quarantine bucket may always be searched at searchtime. Additionally, an indexer may store old, archived data in a frozenbucket that is not capable of being searched at search time. In someembodiments, a frozen bucket may be stored in slower and/or largermemory, such as a hard disk, and may be stored in offline and/or remotestorage.

Moreover, events and buckets may also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. Pat. No. 9,130,971, entitled “SITE-BASEDSEARCH AFFINITY”, issued on 8 Sep. 2015, and in U.S. patent Ser. No.14/266,817, entitled “MULTI-SITE CLUSTERING”, issued on 1 Sep. 2015,each of which is hereby incorporated by reference in its entirety forall purposes.

FIG. 5B is a block diagram of an example data store 501 that includes adirectory for each index (or partition) that contains a portion of datamanaged by an indexer. FIG. 5B further illustrates details of anembodiment of an inverted index 507B and an event reference array 515associated with inverted index 507B.

The data store 501 may correspond to a data store 208 that stores eventsmanaged by an indexer 206 or may correspond to a different data storeassociated with an indexer 206. In the illustrated embodiment, the datastore 501 includes a main directory 503 associated with a main index anda test directory 505 associated with a test index. However, the datastore 501 may include fewer or more directories. In some embodiments,multiple indexes may share a single directory or all indexes may share acommon directory. Additionally, although illustrated as a single datastore 501, it will be understood that the data store 501 may beimplemented as multiple data stores storing different portions of theinformation shown in FIG. 5B. For example, a single index or partitionmay span multiple directories or multiple data stores, and may beindexed or searched by multiple corresponding indexers.

In the illustrated embodiment of FIG. 5B, the index-specific directories503 and 505 include inverted indexes 507A, 507B and 509A, 509B,respectively. The inverted indexes 507A . . . 507B, and 509A . . . 509Bmay be keyword indexes or field-value pair indexes described herein andmay include less or more information that depicted in FIG. 5B.

In some embodiments, the inverted index 507A . . . 507B, and 509A . . .509B may correspond to a distinct time-series bucket that is managed bythe indexer 206 and that contains events corresponding to the relevantindex (e.g., main index, test index). As such, each inverted index maycorrespond to a particular range of time for an index. Additional files,such as high performance indexes for each time-series bucket of anindex, may also be stored in the same directory as the inverted indexes507A . . . 507B, and 509A . . . 509B. In some embodiments inverted index507A . . . 507B, and 509A . . . 509B may correspond to multipletime-series buckets or inverted indexes 507A . . . 507B, and 509A . . .509B may correspond to a single time-series bucket.

Each inverted index 507A . . . 507B, and 509A . . . 509B may include oneor more entries, such as keyword (or token) entries or field-value pairentries. Furthermore, in certain embodiments, the inverted indexes 507A. . . 507B, and 509A . . . 509B may include additional information, suchas a time range 523 associated with the inverted index or an indexidentifier 525 identifying the index associated with the inverted index507A . . . 507B, and 509A . . . 509B. However, each inverted index 507A. . . 507B, and 509A . . . 509B may include less or more informationthan depicted.

Token entries, such as token entries 511 illustrated in inverted index507B, may include a token 511A (e.g., “error,” “itemID,” etc.) and eventreferences 511B indicative of events that include the token. Forexample, for the token “error,” the corresponding token entry includesthe token “error” and an event reference, or unique identifier, for eachevent stored in the corresponding time-series bucket that includes thetoken “error.” In the illustrated embodiment of FIG. 5B, the error tokenentry includes the identifiers 3, 5, 6, 8, 11, and 12 corresponding toevents managed by the indexer 206 and associated with the index main 503that are located in the time-series bucket associated with the invertedindex 507B.

In some cases, some token entries may be default entries, automaticallydetermined entries, or user specified entries. In some embodiments, theindexer 206 may identify each word or string in an event as a distincttoken and generate a token entry for it. In some cases, the indexer 206may identify the beginning and ending of tokens based on punctuation,spaces, as described in greater detail herein. In certain cases, theindexer 206 may rely on user input or a configuration file to identifytokens for token entries 511, etc. It will be understood that anycombination of token entries may be included as a default, automaticallydetermined, and/or included based on user-specified criteria.

Similarly, field-value pair entries, such as field-value pair entries513 shown in inverted index 507B, may include a field-value pair 513Aand event references 513B indicative of events that include a fieldvalue that corresponds to the field-value pair. For example, for afield-value pair source type::sendmail, a field-value pair entry wouldinclude the field-value pair source type::sendmail and a uniqueidentifier, or event reference, for each event stored in thecorresponding time-series bucket that includes a sendmail source type.

In some cases, the field-value pair entries 513 may be default entries,automatically determined entries, or user specified entries. As anon-limiting example, the field-value pair entries for the fields host,source, source type may be included in the inverted indexes 507A . . .507B, and 509A . . . 509B as a default. As such, all of the invertedindexes 507A . . . 507B, and 509A . . . 509B may include field-valuepair entries for the fields host, source, source type. As yet anothernon-limiting example, the field-value pair entries for the IP_addressfield may be user specified and may only appear in the inverted index507B based on user-specified criteria. As another non-limiting example,as the indexer indexes the events, it may automatically identifyfield-value pairs and create field-value pair entries. For example,based on the indexers review of events, it may identify IP_address as afield in each event and add the IP_address field-value pair entries tothe inverted index 507B. It will be understood that any combination offield-value pair entries may be included as a default, automaticallydetermined, or included based on user-specified criteria.

Each unique identifier 517, or event reference, may correspond to aunique event located in the time series bucket. However, the same eventreference may be located in multiple entries. For example, if an eventhas a source type splunkd, host www1 and token “warning,” then theunique identifier for the event will appear in the field-value pairentries source type::splunkd and host::www1, as well as the token entry“warning.” With reference to the illustrated embodiment of FIG. 5B andthe event that corresponds to the event reference 3, the event reference3 is found in the field-value pair entries 513 host::hostA,source::sourceB, source type::source typeA, andIP_address::91.205.189.15 indicating that the event corresponding to theevent references is from hostA, sourceB, of source typeA, and includes91.205.189.15 in the event data.

For some fields, the unique identifier is located in only onefield-value pair entry for a particular field. For example, the invertedindex may include four source type field-value pair entriescorresponding to four different source types of the events stored in abucket (e.g., source types: sendmail, splunkd, web_access, andweb_service). Within those four source type field-value pair entries, anidentifier for a particular event may appear in only one of thefield-value pair entries. With continued reference to the exampleillustrated embodiment of FIG. 5B, since the event reference 7 appearsin the field-value pair entry source type::source typeA, then it doesnot appear in the other field-value pair entries for the source typefield, including source type::source typeB, source type::source typeC,and source type::source typeD.

The event references 517 may be used to locate the events in thecorresponding bucket. For example, the inverted index may include, or beassociated with, an event reference array 515. The event reference array515 may include an array entry 517 for each event reference in theinverted index 507B. Each array entry 517 may include locationinformation 519 of the event corresponding to the unique identifier(non-limiting example: seek address of the event), a timestamp 521associated with the event, or additional information regarding the eventassociated with the event reference, etc.

For each token entry 511 or field-value pair entry 513, the eventreference 501B or unique identifiers may be listed in chronologicalorder or the value of the event reference may be assigned based onchronological data, such as a timestamp associated with the eventreferenced by the event reference. For example, the event reference 1 inthe illustrated embodiment of FIG. 5B may correspond to thefirst-in-time event for the bucket, and the event reference 12 maycorrespond to the last-in-time event for the bucket. However, the eventreferences may be listed in any order, such as reverse chronologicalorder, ascending order, descending order, or some other order, etc.Further, the entries may be sorted. For example, the entries may besorted alphabetically (collectively or within a particular group), byentry origin (e.g., default, automatically generated, user-specified,etc.), by entry type (e.g., field-value pair entry, token entry, etc.),or chronologically by when added to the inverted index, etc. In theillustrated embodiment of FIG. 5B, the entries are sorted first by entrytype and then alphabetically.

As a non-limiting example of how the inverted indexes 507A . . . 507B,and 509A . . . 509B may be used during a data categorization requestcommand, the indexers may receive filter criteria indicating data thatis to be categorized and categorization criteria indicating how the datais to be categorized. Example filter criteria may include, but is notlimited to, indexes (or partitions), hosts, sources, source types, timeranges, field identifier, keywords, etc.

Using the filter criteria, the indexer identifies relevant invertedindexes to be searched. For example, if the filter criteria includes aset of partitions, the indexer may identify the inverted indexes storedin the directory corresponding to the particular partition as relevantinverted indexes. Other means may be used to identify inverted indexesassociated with a partition of interest. For example, in someembodiments, the indexer may review an entry in the inverted indexes,such as an index-value pair entry 513 to determine if a particularinverted index is relevant. If the filter criteria does not identify anypartition, then the indexer may identify all inverted indexes managed bythe indexer as relevant inverted indexes.

Similarly, if the filter criteria includes a time range, the indexer mayidentify inverted indexes corresponding to buckets that satisfy at leasta portion of the time range as relevant inverted indexes. For example,if the time range is last hour then the indexer may identify allinverted indexes that correspond to buckets storing events associatedwith timestamps within the last hour as relevant inverted indexes.

When used in combination, an index filter criterion specifying one ormore partitions and a time range filter criterion specifying aparticular time range may be used to identify a subset of invertedindexes within a particular directory (or otherwise associated with aparticular partition) as relevant inverted indexes. As such, the indexermay focus the processing to only a subset of the total number ofinverted indexes that the indexer manages.

Once the relevant inverted indexes are identified, the indexer mayreview them using any additional filter criteria to identify events thatsatisfy the filter criteria. In some cases, using the known location ofthe directory in which the relevant inverted indexes are located, theindexer may determine that any events identified using the relevantinverted indexes satisfy an index filter criterion. For example, if thefilter criteria includes a partition main, then the indexer maydetermine that any events identified using inverted indexes within thepartition main directory (or otherwise associated with the partitionmain) satisfy the index filter criterion.

Furthermore, based on the time range associated with each invertedindex, the indexer may determine that that any events identified using aparticular inverted index satisfies a time range filter criterion. Forexample, if a time range filter criterion is for the last hour and aparticular inverted index corresponds to events within a time range of50 minutes ago to 35 minutes ago, the indexer may determine that anyevents identified using the particular inverted index satisfy the timerange filter criterion. Conversely, if the particular inverted indexcorresponds to events within a time range of 59 minutes ago to 62minutes ago, the indexer may determine that some events identified usingthe particular inverted index may not satisfy the time range filtercriterion.

Using the inverted indexes, the indexer may identify event references(and therefore events) that satisfy the filter criteria. For example, ifthe token “error” is a filter criterion, the indexer may track all eventreferences within the token entry “error.” Similarly, the indexer mayidentify other event references located in other token entries orfield-value pair entries that match the filter criteria. The system mayidentify event references located in all of the entries identified bythe filter criteria. For example, if the filter criteria include thetoken “error” and field-value pair source type::web_ui, the indexer maytrack the event references found in both the token entry “error” and thefield-value pair entry source type::web_ui. As mentioned previously, insome cases, such as when multiple values are identified for a particularfilter criterion (e.g., multiple sources for a source filter criterion),the system may identify event references located in at least one of theentries corresponding to the multiple values and in all other entriesidentified by the filter criteria. The indexer may determine that theevents associated with the identified event references satisfy thefilter criteria.

In some cases, the indexer may further consult a timestamp associatedwith the event reference to determine whether an event satisfies thefilter criteria. For example, if an inverted index corresponds to a timerange that is partially outside of a time range filter criterion, thenthe indexer may consult a timestamp associated with the event referenceto determine whether the corresponding event satisfies the time rangecriterion. In some embodiments, to identify events that satisfy a timerange, the indexer may review an array, such as the event referencearray 515 that identifies the time associated with the events.Furthermore, as mentioned above using the known location of thedirectory in which the relevant inverted indexes are located (or otherindex identifier), the indexer may determine that any events identifiedusing the relevant inverted indexes satisfy the index filter criterion.

In some cases, based on the filter criteria, the indexer reviews anextraction rule. In certain embodiments, if the filter criteria includesa field name that does not correspond to a field-value pair entry in aninverted index, the indexer may review an extraction rule, which may belocated in a configuration file, to identify a field that corresponds toa field-value pair entry in the inverted index.

For example, the filter criteria includes a field name “sessionID” andthe indexer determines that at least one relevant inverted index doesnot include a field-value pair entry corresponding to the field namesessionID, the indexer may review an extraction rule that identifies howthe sessionID field is to be extracted from a particular host, source,or source type (implicitly identifying the particular host, source, orsource type that includes a sessionID field). The indexer may replacethe field name “sessionID” in the filter criteria with the identifiedhost, source, or source type. In some cases, the field name “sessionID”may be associated with multiples hosts, sources, or source types, inwhich case, all identified hosts, sources, and source types may be addedas filter criteria. In some cases, the identified host, source, orsource type may replace or be appended to a filter criterion, or beexcluded. For example, if the filter criteria includes a criterion forsource S1 and the “sessionID” field is found in source S2, the source S2may replace S1 in the filter criteria, be appended such that the filtercriteria includes source S1 and source S2, or be excluded based on thepresence of the filter criterion source S1. If the identified host,source, or source type is included in the filter criteria, the indexermay then identify a field-value pair entry in the inverted index thatincludes a field value corresponding to the identity of the particularhost, source, or source type identified using the extraction rule.

Once the events that satisfy the filter criteria are identified, thesystem, such as the indexer 206 may categorize the results based on thecategorization criteria. The categorization criteria may includecategories for grouping the results, such as any combination ofpartition, source, source type, or host, or other categories or fieldsas desired.

The indexer may use the categorization criteria to identifycategorization criteria-value pairs or categorization criteria values bywhich to categorize or group the results. The categorizationcriteria-value pairs may correspond to one or more field-value pairentries stored in a relevant inverted index, one or more index-valuepairs based on a directory in which the inverted index is located or anentry in the inverted index (or other means by which an inverted indexmay be associated with a partition), or other criteria-value pair thatidentifies a general category and a particular value for that category.The categorization criteria values may correspond to the value portionof the categorization criteria-value pair.

As mentioned, in some cases, the categorization criteria-value pairs maycorrespond to one or more field-value pair entries stored in therelevant inverted indexes. For example, the categorizationcriteria-value pairs may correspond to field-value pair entries of host,source, and source type (or other field-value pair entry as desired).For instance, if there are ten different hosts, four different sources,and five different source types for an inverted index, then the invertedindex may include ten host field-value pair entries, four sourcefield-value pair entries, and five source type field-value pair entries.The indexer may use the nineteen distinct field-value pair entries ascategorization criteria-value pairs to group the results.

Specifically, the indexer may identify the location of the eventreferences associated with the events that satisfy the filter criteriawithin the field-value pairs, and group the event references based ontheir location. As such, the indexer may identify the particular fieldvalue associated with the event corresponding to the event reference.For example, if the categorization criteria include host and sourcetype, the host field-value pair entries and source type field-value pairentries may be used as categorization criteria-value pairs to identifythe specific host and source type associated with the events thatsatisfy the filter criteria.

In addition, as mentioned, categorization criteria-value pairs maycorrespond to data other than the field-value pair entries in therelevant inverted indexes. For example, if partition or index is used asa categorization criterion, the inverted indexes may not includepartition field-value pair entries. Rather, the indexer may identify thecategorization criteria-value pair associated with the partition basedon the directory in which an inverted index is located, information inthe inverted index, or other information that associates the invertedindex with the partition, etc. As such a variety of methods may be usedto identify the categorization criteria-value pairs from thecategorization criteria.

Accordingly based on the categorization criteria (and categorizationcriteria-value pairs), the indexer may generate groupings based on theevents that satisfy the filter criteria. As a non-limiting example, ifthe categorization criteria includes a partition and source type, thenthe groupings may correspond to events that are associated with eachunique combination of partition and source type. For instance, if thereare three different partitions and two different source types associatedwith the identified events, then the six different groups may be formed,each with a unique partition value-source type value combination.Similarly, if the categorization criteria includes partition, sourcetype, and host and there are two different partitions, three sourcetypes, and five hosts associated with the identified events, then theindexer may generate up to thirty groups for the results that satisfythe filter criteria. Each group may be associated with a uniquecombination of categorization criteria-value pairs (e.g., uniquecombinations of partition value source type value, and host value).

In addition, the indexer may count the number of events associated witheach group based on the number of events that meet the uniquecombination of categorization criteria for a particular group (or matchthe categorization criteria-value pairs for the particular group). Withcontinued reference to the example above, the indexer may count thenumber of events that meet the unique combination of partition, sourcetype, and host for a particular group.

Each indexer communicates the groupings to the search head. The searchhead may aggregate the groupings from the indexers and provide thegroupings for display. In some cases, the groups are displayed based onat least one of the host, source, source type, or partition associatedwith the groupings. In some embodiments, the search head may furtherdisplay the groups based on display criteria, such as a display order ora sort order as described in greater detail above.

As a non-limiting example and with reference to FIG. 5B, consider arequest received by an indexer 206 that includes the following filtercriteria: keyword=error, partition=main, time range=3/1/1716:22.00.000-16:28.00.000, source type=source typeC, host=hostB, and thefollowing categorization criteria: source.

Based on the above criteria, the indexer 206 identifies main directory503 and may ignore_test directory 505 and any other partition-specificdirectories. The indexer determines that inverted partition 507B is arelevant partition based on its location within the main directory 503and the time range associated with it. For sake of simplicity in thisexample, the indexer 206 determines that no other inverted indexes inthe main directory 503, such as inverted index 507A satisfy the timerange criterion.

Having identified the relevant inverted index 507B, the indexer reviewsthe token entries 511 and the field-value pair entries 513 to identifyevent references, or events that satisfy all of the filter criteria.

With respect to the token entries 511, the indexer may review the errortoken entry and identify event references 3, 5, 6, 8, 11, 12, indicatingthat the term “error” is found in the corresponding events. Similarly,the indexer may identify event references 4, 5, 6, 8, 9, 10, 11 in thefield-value pair entry source type::source typeC and event references 2,5, 6, 8, 10, 11 in the field-value pair entry host::hostB. As the filtercriteria did not include a source or an IP_address field-value pair, theindexer may ignore those field-value pair entries.

In addition to identifying event references found in at least one tokenentry or field-value pair entry (e.g., event references 3, 4, 5, 6, 8,9, 10, 11, 12), the indexer may identify events (and corresponding eventreferences) that satisfy the time range criterion using the eventreference array 1614 (e.g., event references 2, 3, 4, 5, 6, 7, 8, 9,10). Using the information obtained from the inverted index 507B(including the event reference array 515), the indexer 206 may identifythe event references that satisfy all of the filter criteria (e.g.,event references 5, 6, 8).

Having identified the events (and event references) that satisfy all ofthe filter criteria, the indexer 206 may group the event referencesusing the received categorization criteria (source). In doing so, theindexer may determine that event references 5 and 6 are located in thefield-value pair entry source::sourceD (or have matching categorizationcriteria-value pairs) and event reference 8 is located in thefield-value pair entry source::sourceC. Accordingly, the indexer maygenerate a sourceC group having a count of one corresponding toreference 8 and a sourceD group having a count of two corresponding toreferences 5 and 6. This information may be communicated to the searchhead. In turn the search head may aggregate the results from the variousindexers and display the groupings. As mentioned above, in someembodiments, the groupings may be displayed based at least in part onthe categorization criteria, including at least one of host, source,source type, or partition.

It will be understood that a change to any of the filter criteria orcategorization criteria may result in different groupings. As a onenon-limiting example, a request received by an indexer 206 that includesthe following filter criteria: partition=main, time range=3/1/17 3/1/1716:21:20.000-16:28:17.000, and the following categorization criteria:host, source, source type would result in the indexer identifying eventreferences 1-12 as satisfying the filter criteria. The indexer wouldthen generate up to 24 groupings corresponding to the 24 differentcombinations of the categorization criteria-value pairs, including host(hostA, hostB), source (sourceA, sourceB, sourceC, sourceD), and sourcetype (source typeA, source typeB, source typeC). However, as there areonly twelve events identifiers in the illustrated embodiment and somefall into the same grouping, the indexer generates eight groups andcounts as follows:

Group 1 (hostA, sourceA, source typeA): 1 (event reference 7)

Group 2 (hostA, sourceA, source typeB): 2 (event references 1, 12)

Group 3 (hostA, sourceA, source typeC): 1 (event reference 4)

Group 4 (hostA, sourceB, source typeA): 1 (event reference 3)

Group 5 (hostA, sourceB, source typeC): 1 (event reference 9)

Group 6 (hostB, sourceC, source typeA): 1 (event reference 2)

Group 7 (hostB, sourceC, source typeC): 2 (event references 8, 11)

Group 8 (hostB, sourceD, source typeC): 3 (event references 5, 6, 10).

As noted, each group has a unique combination of categorizationcriteria-value pairs or categorization criteria values. The indexercommunicates the groups to the search head for aggregation with resultsreceived from other indexers. In communicating the groups to the searchhead, the indexer may include the categorization criteria-value pairsfor each group and the count. In some embodiments, the indexer mayinclude more or less information. For example, the indexer may includethe event references associated with each group and other identifyinginformation, such as the indexer or inverted index used to identify thegroups.

As another non-limiting examples, a request received by an indexer 206that includes the following filter criteria: partition=main, timerange=3/1/17 3/1/17 16:21:20.000-16:28:17.000, source=sourceA, sourceD,and keyword=itemID and the following categorization criteria: host,source, source type would result in the indexer identifying eventreferences 4, 7, and 10 as satisfying the filter criteria, and generatethe following groups:

Group 1 (hostA, sourceA, source typeC): 1 (event reference 4)

Group 2 (hostA, sourceA, source typeA): 1 (event reference 7)

Group 3 (hostB, sourceD, source typeC): 1 (event references 10).

The indexer communicates the groups to the search head for aggregationwith results received from other indexers. As will be understand thereare myriad ways for filtering and categorizing the events and eventreferences. For example, the indexer may review multiple invertedindexes associated with a partition or review the inverted indexes ofmultiple partitions, and categorize the data using any one or anycombination of partition, host, source, source type, or other category,as desired.

Further, if a user interacts with a particular group, the indexer mayprovide additional information regarding the group. For example, theindexer may perform a targeted search or sampling of the events thatsatisfy the filter criteria and the categorization criteria for theselected group, also referred to as the filter criteria corresponding tothe group or filter criteria associated with the group.

In some cases, to provide the additional information, the indexer relieson the inverted index. For example, the indexer may identify the eventreferences associated with the events that satisfy the filter criteriaand the categorization criteria for the selected group and then use theevent reference array 515 to access some or all of the identifiedevents. In some cases, the categorization criteria values orcategorization criteria-value pairs associated with the group becomepart of the filter criteria for the review.

With reference to FIG. 5B for instance, suppose a group is displayedwith a count of six corresponding to event references 4, 5, 6, 8, 10, 11(i.e., event references 4, 5, 6, 8, 10, 11 satisfy the filter criteriaand are associated with matching categorization criteria values orcategorization criteria—value pairs) and a user interacts with the group(e.g., selecting the group, clicking on the group, etc.). In response,the search head communicates with the indexer to provide additionalinformation regarding the group.

In some embodiments, the indexer identifies the event referencesassociated with the group using the filter criteria and thecategorization criteria for the group (e.g., categorization criteriavalues or categorization criteria-value pairs unique to the group).Together, the filter criteria and the categorization criteria for thegroup may be referred to as the filter criteria associated with thegroup. Using the filter criteria associated with the group, the indexeridentifies event references 4, 5, 6, 8, 10, 11.

Based on a sampling criteria, discussed in greater detail above, theindexer may determine that it will analyze a sample of the eventsassociated with the event references 4, 5, 6, 8, 10, 11. For example,the sample may include analyzing event data associated with the eventreferences 5, 8, 10. In some embodiments, the indexer may use the eventreference array 515 to access the event data associated with the eventreferences 5, 8, 10. Once accessed, the indexer may compile the relevantinformation and provide it to the search head for aggregation withresults from other indexers. By identifying events and sampling eventdata using the inverted indexes, the indexer may reduce the amount ofactual data this is analyzed and the number of events that are accessedin order to generate the summary of the group and provide a response inless time.

FIG. 6A is a flow diagram of an example method that illustrates how asearch head and indexers perform a search query, in accordance withexample embodiments. At block 602, a search head receives a search queryfrom a client. At block 604, the search head analyzes the search queryto determine what portion(s) of the query may be delegated to indexersand what portions of the query may be executed locally by the searchhead. At block 606, the search head distributes the determined portionsof the query to the appropriate indexers. In some embodiments, a searchhead cluster may take the place of an independent search head where eachsearch head in the search head cluster coordinates with peer searchheads in the search head cluster to schedule jobs, replicate searchresults, update configurations, fulfill search requests, etc. In someembodiments, the search head (or each search head) communicates with amaster node (also known as a cluster master, not shown in FIG. 2) thatprovides the search head with a list of indexers to which the searchhead may distribute the determined portions of the query. The masternode maintains a list of active indexers and may also designate whichindexers may have responsibility for responding to queries over certainsets of events. A search head may communicate with the master nodebefore the search head distributes queries to indexers to discover theaddresses of active indexers.

At block 608, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria may include matching keywords or specific valuesfor certain fields. The searching operations at block 608 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In some embodiments, one or morerules for extracting field values may be specified as part of a sourcetype definition in a configuration file. The indexers may then eithersend the relevant events back to the search head, or use the events todetermine a partial result, and send the partial result back to thesearch head.

At block 610, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Insome examples, the results of the query are indicative of performance orsecurity of the IT environment and may help improve the performance ofcomponents in the IT environment. This final result may comprisedifferent types of data depending on what the query requested. Forexample, the results may include a listing of matching events returnedby the query, or some type of visualization of the data from thereturned events. In another example, the final result may include one ormore calculated values derived from the matching events.

The results generated by the system 108 may be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head may also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head may determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headmay perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries, which may beparticularly helpful for queries that are performed on a periodic basis.

Various embodiments of the present disclosure may be implemented using,or in conjunction with, a pipelined command language. A pipelinedcommand language is a language in which a set of inputs or data isoperated on by a first command in a sequence of commands, and thensubsequent commands in the order they are arranged in the sequence. Suchcommands may include any type of functionality for operating on data,such as retrieving, searching, filtering, aggregating, processing,transmitting, and the like. As described herein, a query may thus beformulated in a pipelined command language and include any number ofordered or unordered commands for operating on data.

Splunk Processing Language (SPL) is an example of a pipelined commandlanguage in which a set of inputs or data is operated on by any numberof commands in a particular sequence. A sequence of commands, or commandsequence, may be formulated such that the order in which the commandsare arranged defines the order in which the commands are applied to aset of data or the results of an earlier executed command. For example,a first command in a command sequence may operate to search or filterfor specific data in particular set of data. The results of the firstcommand may then be passed to another command listed later in thecommand sequence for further processing.

In various embodiments, a query may be formulated as a command sequencedefined in a command line of a search UI. In some embodiments, a querymay be formulated as a sequence of SPL commands. Some or all of the SPLcommands in the sequence of SPL commands may be separated from oneanother by a pipe symbol “|”. In such embodiments, a set of data, suchas a set of events, may be operated on by a first SPL command in thesequence, and then a subsequent SPL command following a pipe symbol “|”after the first SPL command operates on the results produced by thefirst SPL command or other set of data, and so on for any additional SPLcommands in the sequence. As such, a query formulated using SPLcomprises a series of consecutive commands that are delimited by pipe“|” characters. The pipe character indicates to the system that theoutput or result of one command (to the left of the pipe) should be usedas the input for one of the subsequent commands (to the right of thepipe). This enables formulation of queries defined by a pipeline ofsequenced commands that refines or enhances the data at each step alongthe pipeline until the desired results are attained. Accordingly,various embodiments described herein may be implemented with SplunkProcessing Language (SPL) used in conjunction with the SPLUNK®ENTERPRISE system.

While a query may be formulated in many ways, a query may start with asearch command and one or more corresponding search terms at thebeginning of the pipeline. Such search terms may include any combinationof keywords, phrases, times, dates, Boolean expressions, fieldname-fieldvalue pairs, etc. that specify which results should be obtained from anindex. The results may then be passed as inputs into subsequent commandsin a sequence of commands by using, for example, a pipe character. Thesubsequent commands in a sequence may include directives for additionalprocessing of the results once it has been obtained from one or moreindexes. For example, commands may be used to filter unwantedinformation out of the results, extract more information, evaluate fieldvalues, calculate statistics, reorder the results, create an alert,create summary of the results, or perform some type of aggregationfunction. In some embodiments, the summary may include a graph, chart,metric, or other visualization of the data. An aggregation function mayinclude analysis or calculations to return an aggregate value, such asan average value, a sum, a maximum value, a root mean square,statistical values, and the like.

Due to its flexible nature, use of a pipelined command language invarious embodiments is advantageous because it may perform “filtering”as well as “processing” functions. In other words, a single query mayinclude a search command and search term expressions, as well asdata-analysis expressions. For example, a command at the beginning of aquery may perform a “filtering” step by retrieving a set of data basedon a condition (e.g., records associated with server response times ofless than 1 microsecond). The results of the filtering step may then bepassed to a subsequent command in the pipeline that performs a“processing” step (e.g., calculation of an aggregate value related tothe filtered events such as the average response time of servers withresponse times of less than 1 microsecond). Furthermore, the searchcommand may allow events to be filtered by keyword as well as fieldvalue criteria. For example, a search command may filter out all eventscontaining the word “warning” or filter out all events where a fieldvalue associated with a field “clientip” is “10.0.1.2.”

The results obtained or generated in response to a command in a querymay be considered a set of results data. The set of results data may bepassed from one command to another in any data format. In oneembodiment, the set of result data may be in the form of a dynamicallycreated table. Each command in a particular query may redefine the shapeof the table. In some implementations, an event retrieved from an indexin response to a query may be considered a row with a column for eachfield value. Columns contain basic information about the data and alsomay contain data that has been dynamically extracted at search time.

FIG. 6B provides a visual representation of the manner in which apipelined command language or query operates in accordance with thedisclosed embodiments. The query 630 may be inputted by the user into asearch. The query comprises a search, the results of which are piped totwo commands (namely, command 1 and command 2) that follow the searchstep.

Disk 622 represents the event data in the raw record data store.

When a user query is processed, a search step will precede other queriesin the pipeline in order to generate a set of events at block 640. Forexample, the query may comprise search terms “source type=syslog ERROR”at the front of the pipeline as shown in FIG. 6B. Intermediate resultstable 624 shows fewer rows because it represents the subset of eventsretrieved from the index that matched the search terms “sourcetype=syslog ERROR” from search command 630. By way of further example,instead of a search step, the set of events at the head of the pipelinemay be generating by a call to a pre-existing inverted index (as will beexplained later).

At block 642, the set of events generated in the first part of the querymay be piped to a query that searches the set of events for field-valuepairs or for keywords. For example, the second intermediate resultstable 626 shows fewer columns, representing the result of the topcommand, “top user” which summarizes the events into a list of the top10 users and displays the user, count, and percentage.

Finally, at block 644, the results of the prior stage may be pipelinedto another stage where further filtering or processing of the data maybe performed, e.g., preparing the data for display purposes, filteringthe data based on a condition, performing a mathematical calculationwith the data, etc. As shown in FIG. 6B, the “fields-percent” part ofcommand 630 removes the column that shows the percentage, thereby,leaving a final results table 628 without a percentage column. Indifferent embodiments, other query languages, such as the StructuredQuery Language (“SQL”), may be used to create a query.

The search head 210 allows users to search and visualize eventsgenerated from machine data received from homogenous data sources. Thesearch head 210 also allows users to search and visualize eventsgenerated from machine data received from heterogeneous data sources.The search head 210 includes various mechanisms, which may additionallyreside in an indexer 206, for processing a query. A query language maybe used to create a query, such as any suitable pipelined querylanguage. For example, Splunk Processing Language (SPL) may be utilizedto make a query. SPL is a pipelined search language in which a set ofinputs is operated on by a first command in a command line, and then asubsequent command following the pipe symbol “|” operates on the resultsproduced by the first command, and so on for additional commands. Otherquery languages, such as the Structured Query Language (“SQL”), may beused to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for fields in the events beingsearched. The search head 210 obtains extraction rules that specify howto extract a value for fields from an event. Extraction rules maycomprise regex rules that specify how to extract values for the fieldscorresponding to the extraction rules. In addition to specifying how toextract field values, the extraction rules may also include instructionsfor deriving a field value by performing a function on a characterstring or value retrieved by the extraction rule. For example, anextraction rule may truncate a character string or convert the characterstring into a different data format. In some cases, the query itself mayspecify one or more extraction rules.

The search head 210 may apply the extraction rules to events that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules may beapplied to all the events in a data store or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules may be used to extract one or morevalues for a field from events by parsing the portions of machine datain the events and examining the data for one or more patterns ofcharacters, numbers, delimiters, etc., that indicate where the fieldbegins and, optionally, ends.

FIG. 7A is a diagram of an example scenario where a common customeridentifier is found among log data received from three disparate datasources, in accordance with example embodiments. In this example, a usersubmits an order for merchandise using a vendor's shopping applicationprogram 701 running on the user's system. In this example, the order wasnot delivered to the vendor's server due to a resource exception at thedestination server that is detected by the middleware code 702. The userthen sends a message to the customer support server 703 to complainabout the order failing to complete. The three systems 701, 702, and 703are disparate systems that do not have a common logging format. Theorder application 701 sends log data 704 to the data collection,indexing, and visualization system in one format, the middleware code702 sends error log data 705 in a second format, and the support server703 sends log data 706 in a third format.

Using the log data received at one or more indexers 206 from the threesystems, the vendor may uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator may query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of the systems.There is a semantic relationship between the customer ID field valuesgenerated by the three systems. The search head 210 requests events fromthe one or more indexers 206 to gather relevant events from the threesystems. The search head 210 then applies extraction rules to the eventsin order to extract field values that it may correlate. The search headmay apply a different extraction rule to each set of events from eachsystem when the event format differs among systems. In this example, theuser interface may display to the administrator the events correspondingto the common customer ID field values 707, 708, and 709, therebyproviding the administrator with insight into a customer's experience.

Note that query results may be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, avisualization (e.g., a graph or chart) generated from the values, andthe like.

The search system enables users to run queries against the stored datato retrieve events that meet criteria specified in a query, such ascontaining certain keywords or having specific values in defined fields.FIG. 7B illustrates the manner in which keyword searches and fieldsearches are processed in accordance with disclosed embodiments.

If a user inputs a search query into search bar 710 that includes onlykeywords (also known as “tokens”), e.g., the keyword “error” or“warning”, the query search engine of the data collection, indexing, andvisualization system searches for those keywords directly in the eventdata 711 stored in the raw record data store. Note that while FIG. 7Bonly illustrates four events 712, 713, 714, 715, the raw record datastore (corresponding to data store 208 in FIG. 2) may contain recordsfor millions of events.

As disclosed above, an indexer may optionally generate a keyword indexto facilitate fast keyword searching for event data. The indexerincludes the identified keywords in an index, which associates eachstored keyword with reference pointers to events containing that keyword(or to locations within events where that keyword is located, otherlocation identifiers, etc.). When an indexer subsequently receives akeyword-based query, the indexer may access the keyword index to quicklyidentify events containing the keyword. For example, if the keyword“HTTP” was indexed by the indexer at index time, and the user searchesfor the keyword “HTTP”, the events 712, 713, and 714, will be identifiedbased on the results returned from the keyword index. As noted above,the index contains reference pointers to the events containing thekeyword, which allows for efficient retrieval of the relevant eventsfrom the raw record data store.

If a user searches for a keyword that has not been indexed by theindexer, the data collection, indexing, and visualization system wouldnevertheless be able to retrieve the events by searching the event datafor the keyword in the raw record data store directly as shown in FIG.7B. For example, if a user searches for the keyword “frank”, and thename “frank” has not been indexed at index time, the data collection,indexing, and visualization system will search the event data directlyand return the first event 712. Note that whether the keyword has beenindexed at index time or not, in both cases the raw data of the events712-715 is accessed from the raw data record store to service thekeyword search. In the case where the keyword has been indexed, theindex will contain a reference pointer that will allow for a moreefficient retrieval of the event data from the data store. If thekeyword has not been indexed, the search engine will need to searchthrough all the records in the data store to service the search.

In most cases, however, in addition to keywords, a user's search willalso include fields. The term “field” refers to a location in the eventdata containing one or more values for a specific data item. Often, afield is a value with a fixed, delimited position on a line, or a nameand value pair, where there is a single value to each field name. Afield may also be multivalued, that is, it may appear more than once inan event and have a different value for each appearance, e.g., emailaddress fields. Fields are searchable by the field name or fieldname-value pairs. Some examples of fields are “clientip” for IPaddresses accessing a web server, or the “From” and “To” fields in emailaddresses.

By way of further example, consider the search, “status=404”. Thissearch query finds events with “status” fields that have a value of“404.” When the search is run, the search engine does not look forevents with any other “status” value. It also does not look for eventscontaining other fields that share “404” as a value. As a result, thesearch returns a set of results that are more focused than if “404” hadbeen used in the search string as part of a keyword search. Note alsothat fields may appear in events as “key=value” pairs such as“user_name=Bob.” But in most cases, field values appear in fixed,delimited positions without identifying keys. For example, the datastore may contain events where the “user_name” value always appears byitself after the timestamp as illustrated by the following string: “Nov15 09:33:22 johnmedlock.”

The data collection, indexing, and visualization system advantageouslyallows for search time field extraction. In other words, fields may beextracted from the event data at search time using late-binding schemaas opposed to at data ingestion time, which was a major limitation ofthe prior art systems.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules may comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself may specify one or more extraction rules.

FIG. 7B illustrates the manner in which configuration files may be usedto configure custom fields at search time in accordance with thedisclosed embodiments. In response to receiving a search query, the datacollection, indexing, and visualization system determines if the queryreferences a “field.” For example, a query may request a list of eventswhere the “clientip” field equals “127.0.0.1.” If the query itself doesnot specify an extraction rule and if the field is not a metadata field,e.g., time, host, source, source type, etc., then in order to determinean extraction rule, the search engine may, in one or more embodiments,need to locate configuration file 716 during the execution of the searchas shown in FIG. 7B.

Configuration file 716 may contain extraction rules for all the variousfields that are not metadata fields, e.g., the “clientip” field. Theextraction rules may be inserted into the configuration file in avariety of ways. In some embodiments, the extraction rules may compriseregular expression rules that are manually entered in by the user.Regular expressions match patterns of characters in text and are usedfor extracting custom fields in text.

In one or more embodiments, as noted above, a field extractor may beconfigured to automatically generate extraction rules for certain fieldvalues in the events when the events are being created, indexed, orstored, or possibly at a later time. In one embodiment, a user may beable to dynamically create custom fields by highlighting portions of asample event that should be extracted as fields using a graphical userinterface. The system would then generate a regular expression thatextracts those fields from similar events and store the regularexpression as an extraction rule for the associated field in theconfiguration file 716.

In some embodiments, the indexers may automatically discover certaincustom fields at index time and the regular expressions for those fieldswill be automatically generated at index time and stored as part ofextraction rules in configuration file 716. For example, fields thatappear in the event data as “key=value” pairs may be automaticallyextracted as part of an automatic field discovery process. Note thatthere may be several other ways of adding field definitions toconfiguration files in addition to the methods discussed herein.

The search head 210 may apply the extraction rules derived fromconfiguration file 716 to event data that it receives from indexers 206.Indexers 206 may apply the extraction rules from the configuration fileto events in an associated data store 208. Extraction rules may beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules may be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

In one more embodiments, the extraction rule in configuration file 716will also need to define the type or set of events that the rule appliesto. Because the raw record data store will contain events from multipleheterogeneous sources, multiple events may contain the same fields indifferent locations because of discrepancies in the format of the datagenerated by the various sources. Furthermore, certain events may notcontain a particular field at all. For example, event 715 also contains“clientip” field, however, the “clientip” field is in a different formatfrom the events 712, 713, and 714. To address the discrepancies in theformat and content of the different types of events, the configurationfile will also need to specify the set of events that an extraction ruleapplies to, e.g., extraction rule 717 specifies a rule for filtering bythe type of event and contains a regular expression for parsing out thefield value. Accordingly, each extraction rule will pertain to only aparticular type of event. If a particular field, e.g., “clientip” occursin multiple events, each of those types of events would need its owncorresponding extraction rule in the configuration file 716 and each ofthe extraction rules would comprise a different regular expression toparse out the associated field value. The most common way to categorizeevents is by source type because events generated by a particular sourcemay have the same format.

The field extraction rules stored in configuration file 716 performsearch-time field extractions. For example, for a query that requests alist of events with source type “access_combined” where the “clientip”field equals “127.0.0.1,” the query search engine would first locate theconfiguration file 716 to retrieve extraction rule 717 that would allowit to extract values associated with the “clientip” field from the eventdata 720 “where the source type is “access_combined. After the“clientip” field has been extracted from all the events comprising the“clientip” field where the source type is “access_combined,” the querysearch engine may then execute the field criteria by performing thecompare operation to filter out the events where the “clientip” fieldequals “127.0.0.1.” In the example shown in FIG. 7B, the events 712,713, and 714 would be returned in response to the user query. In thismanner, the search engine may service queries containing field criteriain addition to queries containing keyword criteria (as explained above).

The configuration file may be created during indexing. It may either bemanually created by the user or automatically generated with certainpredetermined field extraction rules. As discussed above, the events maybe distributed across several indexers, wherein each indexer may beresponsible for storing and searching a subset of the events containedin a corresponding data store. In a distributed indexer system, eachindexer would need to maintain a local copy of the configuration filethat is synchronized periodically across the various indexers.

The ability to add schema to the configuration file at search timeresults in increased efficiency. A user may create new fields at searchtime and simply add field definitions to the configuration file. As auser learns more about the data in the events, the user may continue torefine the late-binding schema by adding new fields, deleting fields, ormodifying the field extraction rules in the configuration file for usethe next time the schema is used by the system. Because the datacollection, indexing, and visualization system maintains the underlyingraw data and uses late-binding schema for searching the raw data, itenables a user to continue investigating and learn valuable insightsabout the raw data long after data ingestion time.

The ability to add multiple field definitions to the configuration fileat search time also results in increased flexibility. For example,multiple field definitions may be added to the configuration file tocapture the same field across events generated by different sourcetypes. This allows the data collection, indexing, and visualizationsystem to search and correlate data across heterogeneous sourcesflexibly and efficiently.

Further, by providing the field definitions for the queried fields atsearch time, the configuration file 716 allows the record data store tobe field searchable. In other words, the raw record data store may besearched using keywords as well as fields, wherein the fields aresearchable name/value pairings that distinguish one event from anotherand may be defined in configuration file 716 using extraction rules. Incomparison to a search containing field names, a keyword search does notneed the configuration file and may search the event data directly asshown in FIG. 7B.

It should also be noted that any events filtered out by performing asearch-time field extraction using a configuration file may be furtherprocessed by directing the results of the filtering step to a processingstep using a pipelined search language. Using the prior example, a usercould pipeline the results of the compare step to an aggregate functionby asking the query search engine to count the number of events wherethe “clientip” field equals “127.0.0.1.”

FIG. 8A is an interface diagram of an example user interface for asearch screen 800, in accordance with example embodiments. Search screen800 includes a search bar 802 that accepts user input in the form of asearch string. It also includes a time range picker 812 that enables theuser to specify a time range for the search. For historical searches(e.g., searches based on a particular historical time range), the usermay select a specific time range, or alternatively a relative timerange, such as “today,” “yesterday” or “last week.” For real-timesearches (e.g., searches whose results are based on data received inreal-time), the user may select the size of a preceding time window tosearch for real-time events. Search screen 800 also initially displays a“data summary” dialog as is illustrated in FIG. 8B that enables the userto select different sources for the events, such as by selectingspecific hosts and log files.

After the search is executed, the search screen 800 in FIG. 8A maydisplay the results through search results tabs 804, wherein searchresults tabs 804 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 8A displays a timeline graph 805 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. The events tab also displays anevents list 808 that enables a user to view the machine data in each ofthe returned events.

The events tab additionally displays a sidebar that is an interactivefield picker 806. The field picker 806 may be displayed to a user inresponse to the search being executed and allows the user to furtheranalyze the search results based on the fields in the events of thesearch results. The field picker 806 includes field names that referencefields present in the events in the search results. The field picker maydisplay any Selected Fields 820 that a user has pre-selected for display(e.g., host, source, source type) and may also display any InterestingFields 822 that the system determines may be interesting to the userbased on pre-specified criteria (e.g., action, bytes, categoryid,clientip, date_hour, date_mday, date_minute, etc.). The field pickeralso provides an option to display field names for all the fieldspresent in the events of the search results using the All Fields control824.

Each field name in the field picker 806 has a value type identifier tothe left of the field name, such as value type identifier 826. A valuetype identifier identifies the type of value for the respective field,such as an “a” for fields that include literal values or a “#” forfields that include numerical values.

Each field name in the field picker also has a unique value count to theright of the field name, such as unique value count 828. The uniquevalue count indicates the number of unique values for the respectivefield in the events of the search results.

Each field name is selectable to view the events in the search resultsthat have the field referenced by that field name. For example, a usermay select the “host” field name, and the events shown in the eventslist 808 will be updated with events in the search results that have thefield that is reference by the field name “host.”

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge used to build a variety of specialized searches of thosedatasets. Those searches, in turn, may be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.An object is defined by constraints and attributes. An object'sconstraints are search criteria that define the set of events to beoperated on by running a search having that search criteria at the timethe data model is selected. An object's attributes are the set of fieldsto be exposed for operating on that set of events generated by thesearch criteria.

Objects in data models may be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Child objects inherit constraints andattributes from their parent objects and may have additional constraintsand attributes of their own. Child objects provide a way of filteringevents from parent objects. Because a child object may provide anadditional constraint in addition to the constraints it has inheritedfrom its parent object, the dataset it represents may be a subset of thedataset that its parent represents. For example, a first data modelobject may define a broad set of data pertaining to e-mail activitygenerally, and another data model object may define specific datasetswithin the broad dataset, such as a subset of the e-mail data pertainingspecifically to e-mails sent. For example, a user may simply select an“e-mail activity” data model object to access a dataset relating toe-mails generally (e.g., sent or received), or select an “e-mails sent”data model object (or data sub-model object) to access a datasetrelating to e-mails sent.

Because a data model object is defined by its constraints (e.g., a setof search criteria) and attributes (e.g., a set of fields), a data modelobject may be used to quickly search data to identify a set of eventsand to identify a set of fields to be associated with the set of events.For example, an “e-mails sent” data model object may specify a searchfor events relating to e-mails that have been sent, and specify a set offields that are associated with the events. Thus, a user may retrieveand use the “e-mails sent” data model object to quickly search sourcedata for events relating to sent e-mails, and may be provided with alisting of the set of fields relevant to the events in a user interfacescreen.

Examples of data models may include electronic mail, authentication,databases, intrusion detection, malware, application state, alerts,compute inventory, network sessions, network traffic, performance,audits, updates, vulnerabilities, etc. Data models and their objects maybe designed by knowledge managers in an organization, and they mayenable downstream users to quickly focus on a specific set of data. Auser iteratively applies a model development tool (not shown in FIG. 8A)to prepare a query that defines a subset of events and assigns an objectname to that subset. A child subset is created by further limiting aquery that generated a parent sub set.

Data definitions in associated schemas may be taken from the commoninformation model (CIM) or may be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand may include fields not present in parents. A model developer mayselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules may be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. Pat. No.9,128,980, entitled “GENERATION OF A DATA MODEL APPLIED TO QUERIES”,issued on 8 Sep. 2015, and U.S. Pat. No. 9,589,012, entitled “GENERATIONOF A DATA MODEL APPLIED TO OBJECT QUERIES”, issued on 7 Mar. 2017, eachof which is hereby incorporated by reference in its entirety for allpurposes.

A data model may also include reports. One or more report formats may beassociated with a particular data model and be made available to runagainst the data model. A user may use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In some embodiments, the data collection, indexing, and visualizationsystem 108 provides the user with the ability to produce reports (e.g.,a table, chart, visualization, etc.) without having to enter SPL, SQL,or other query language terms into a search screen. Data models are usedas the basis for the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data may be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes. Datavisualizations also may be generated in a variety of formats, byreference to the data model. Reports, data visualizations, and datamodel objects may be saved and associated with the data model for futureuse. The data model object may be used to perform searches of otherdata.

FIGS. 9-15 are interface diagrams of example report generation userinterfaces, in accordance with example embodiments. The reportgeneration process may be driven by a predefined data model object, suchas a data model object defined and/or saved via a reporting applicationor a data model object obtained from another source. A user may load asaved data model object using a report editor. For example, the initialsearch query and fields used to drive the report editor may be obtainedfrom a data model object. The data model object that is used to drive areport generation process may define a search and a set of fields. Uponloading of the data model object, the report generation process mayenable a user to use the fields (e.g., the fields defined by the datamodel object) to define criteria for a report (e.g., filters, splitrows/columns, aggregates, etc.) and the search may be used to identifyevents (e.g., to identify events responsive to the search) used togenerate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 9 illustrates an example interactive data modelselection graphical user interface 900 of a report editor that displaysa listing of available data models 901. The user may select one of thedata models 902.

FIG. 10 illustrates an example data model object selection graphicaluser interface 1000 that displays available data objects 1001 for theselected data object model 902. The user may select one of the displayeddata model objects 1002 for use in driving the report generationprocess.

Once a data model object is selected by the user, a user interfacescreen 1100 shown in FIG. 11A may display an interactive listing ofautomatic field identification options 1101 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 1102, the “SelectedFields” option 1103, or the “Coverage” option (e.g., fields with atleast a specified % of coverage) 1104). If the user selects the “AllFields” option 1102, all of the fields identified from the events thatwere returned in response to an initial search query may be selected.That is, for example, all of the fields of the identified data modelobject fields may be selected. If the user selects the “Selected Fields”option 1103, only the fields from the fields of the identified datamodel object fields that are selected by the user may be used. If theuser selects the “Coverage” option 1104, only the fields of theidentified data model object fields meeting a specified coveragecriteria may be selected. A percent coverage may refer to the percentageof events returned by the initial search query that a given fieldappears in. Thus, for example, if an object dataset includes 10,000events returned in response to an initial search query, and the“avg_age” field appears in 854 of those 10,000 events, then the“avg_age” field would have a coverage of 8.54% for that object dataset.If, for example, the user selects the “Coverage” option and specifies acoverage value of 2%, only fields having a coverage value equal to orgreater than 2% may be selected. The number of fields corresponding toeach selectable option may be displayed in association with each option.For example, “97” displayed next to the “All Fields” option 1102indicates that 97 fields will be selected if the “All Fields” option isselected. The “3” displayed next to the “Selected Fields” option 1103indicates that 3 of the 97 fields will be selected if the “SelectedFields” option is selected. The “49” displayed next to the “Coverage”option 1104 indicates that 49 of the 97 fields (e.g., the 49 fieldshaving a coverage of 2% or greater) will be selected if the “Coverage”option is selected. The number of fields corresponding to the “Coverage”option may be dynamically updated based on the specified percent ofcoverage.

FIG. 11B illustrates an example graphical user interface screen 1105displaying the reporting application's “Report Editor” page. The screenmay display interactive elements for defining various elements of areport. For example, the page includes a “Filters” element 1106, a“Split Rows” element 1107, a “Split Columns” element 1108, and a “ColumnValues” element 1109. The page may include a list of search results1111. In this example, the Split Rows element 1107 is expanded,revealing a listing of fields 1110 that may be used to define additionalcriteria (e.g., reporting criteria). The listing of fields 1110 maycorrespond to the selected fields. That is, the listing of fields 1110may list only the fields previously selected, either automaticallyand/or manually by a user. FIG. 11C illustrates a formatting dialogue1112 that may be displayed upon selecting a field from the listing offields 1110. The dialogue may be used to format the display of theresults of the selection (e.g., label the column for the selected fieldto be displayed as “component”).

FIG. 11D illustrates an example graphical user interface screen 1105including a table of results 1113 based on the selected criteriaincluding splitting the rows by the “component” field. A column 1114having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row for aparticular field, such as the value “BucketMover” for the field“component”) occurs in the set of events responsive to the initialsearch query.

FIG. 12 illustrates an example graphical user interface screen 1200 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1201 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1202. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1206. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1203. A count of the number of successful purchases foreach product is displayed in column 1204. These statistics may beproduced by filtering the search results by the product name, findingall occurrences of a successful purchase in a field within the eventsand generating a total of the number of occurrences. A sum of the totalsales is displayed in column 1205, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 13 illustrates an example graphical user interface 1300 thatdisplays a set of components and associated statistics 1301. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.), wherethe format of the graph may be selected using the user interfacecontrols 1302 along the left panel of the user interface 1300. FIG. 14illustrates an example of a bar chart visualization 1400 of an aspect ofthe statistical data 1301. FIG. 15 illustrates a scatter plotvisualization 1500 of an aspect of the statistical data 1301.

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally-processed data “on thefly” at search time using a late-binding schema, instead of storingpre-specified portions of the data in a database at ingestion time. Thisflexibility enables a user to see valuable insights, correlate data, andperform subsequent queries to examine interesting aspects of the datathat may not have been apparent at ingestion time.

However, performing extraction and analysis operations at search timemay involve a large amount of data and require a large number ofcomputational operations, which may cause delays in processing thequeries. Advantageously, the data collection, indexing, andvisualization system also employs a number of unique accelerationtechniques that have been developed to speed up analysis operationsperformed at search time. These techniques include: (1) performingsearch operations in parallel across multiple indexers; (2) using akeyword index; (3) using a high performance analytics store; and (4)accelerating the process of generating reports. These novel techniquesare described in more detail below.

To facilitate faster query processing, a query may be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 16 is an example search query receivedfrom a client and executed by search peers, in accordance with exampleembodiments. FIG. 16 illustrates how a search query 1602 received from aclient at a search head 210 may split into two phases, including: (1)subtasks 1604 (e.g., data retrieval or simple filtering) that may beperformed in parallel by indexers 206 for execution, and (2) a searchresults aggregation operation 1606 to be executed by the search headwhen the results are ultimately collected from the indexers.

During operation, upon receiving search query 1602, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 1602 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 1604, and then distributes searchquery 1604 to distributed indexers, which are also referred to as“search peers” or “peer indexers.” Note that search queries maygenerally specify search criteria or operations to be performed onevents that meet the search criteria. Search queries may also specifyfield names, as well as search criteria for the values in the fields oroperations to be performed on the values in the fields. Moreover, thesearch head may distribute the full search query to the search peers asillustrated in FIG. 6A, or may alternatively distribute a modifiedversion (e.g., a more restricted version) of the search query to thesearch peers. In this example, the indexers are responsible forproducing the results and sending them to the search head. After theindexers return the results to the search head, the search headaggregates the received results 1606 to form a single search result set.By executing the query in this manner, the system effectivelydistributes the computational operations across the indexers whileminimizing data transfers.

As described above with reference to the flow charts in FIG. 5A and FIG.6A, data collection, indexing, and visualization system 108 mayconstruct and maintain one or more keyword indices to quickly identifyevents containing specific keywords. This technique may greatly speed upthe processing of queries involving specific keywords. As mentionedabove, to build a keyword index, an indexer first identifies a set ofkeywords. Then, the indexer includes the identified keywords in anindex, which associates each stored keyword with references to eventscontaining that keyword, or to locations within events where thatkeyword is located. When an indexer subsequently receives akeyword-based query, the indexer may access the keyword index to quicklyidentify events containing the keyword.

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the events and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table may keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemmay examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system may use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system may maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table may be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query may be initiated by a user, ormay be scheduled to occur automatically at specific time intervals. Aperiodic query may also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system may use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results may then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. Pat. No.9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCE ANALYTICS STOREWITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO AN EVENT QUERY”,issued on 8 Sep. 2015, and U.S. patent application Ser. No. 14/815,973,entitled “GENERATING AND STORING SUMMARIZATION TABLES FOR SETS OFSEARCHABLE EVENTS”, filed on 1 Aug. 2015, each of which is herebyincorporated by reference in its entirety for all purposes.

To speed up certain types of queries, e.g., frequently encounteredqueries or computationally intensive queries, some embodiments of system108 create a high performance analytics store, which is referred to as a“summarization table,” (also referred to as a “lexicon” or “invertedindex”) that contains entries for specific field-value pairs. Each ofthese entries keeps track of instances of a specific value in a specificfield in the event data and includes references to events containing thespecific value in the specific field. For example, an example entry inan inverted index may keep track of occurrences of the value “94107” ina “ZIP code” field of a set of events and the entry includes referencesto all of the events that contain the value “94107” in the ZIP codefield. Creating the inverted index data structure avoids needing toincur the computational overhead each time a statistical query needs tobe run on a frequently encountered field-value pair. In order toexpedite queries, in most embodiments, the search engine will employ theinverted index separate from the raw record data store to generateresponses to the received queries.

Note that the term “summarization table” or “inverted index” as usedherein is a data structure that may be generated by an indexer thatincludes at least field names and field values that have been extractedand/or indexed from event records. An inverted index may also includereference values that point to the location(s) in the field searchabledata store where the event records that include the field may be found.Also, an inverted index may be stored using well-known compressiontechniques to reduce its storage size.

Further, note that the term “reference value” (also referred to as a“posting value”) as used herein is a value that references the locationof a source record in the field searchable data store. In someembodiments, the reference value may include additional informationabout each record, such as timestamps, record size, meta-data, or thelike. Each reference value may be a unique identifier which may be usedto access the event data directly in the field searchable data store. Insome embodiments, the reference values may be ordered based on eachevent record's timestamp. For example, if numbers are used asidentifiers, they may be sorted so event records having a latertimestamp always have a lower valued identifier than event records withan earlier timestamp, or vice-versa. Reference values are often includedin inverted indexes for retrieving and/or identifying event records.

In one or more embodiments, an inverted index is generated in responseto a user-initiated collection query. The term “collection query” asused herein refers to queries that include commands that generatesummarization information and inverted indexes (or summarization tables)from event records stored in the field searchable data store.

Note that a collection query is a special type of query that may beuser-generated and is used to create an inverted index. A collectionquery is not the same as a query that is used to call up or invoke apre-existing inverted index. In one or more embodiments, a query maycomprise an initial step that calls up a pre-generated inverted index onwhich further filtering and processing may be performed. For example,referring back to FIG. 6B, a set of events may be generated at block 640by either using a “collection” query to create a new inverted index orby calling up a pre-generated inverted index. A query with severalpipelined steps will start with a pre-generated index to accelerate thequery.

FIG. 7C illustrates the manner in which an inverted index is created andused in accordance with the disclosed embodiments. As shown in FIG. 7C,an inverted index 722 may be created in response to a user-initiatedcollection query using the event data 723 stored in the raw record datastore. For example, a non-limiting example of a collection query mayinclude “collect clientip=127.0.0.1” which may result in an invertedindex 722 being generated from the event data 723 as shown in FIG. 7C.Each entry in inverted index 722 includes an event reference value thatreferences the location of a source record in the field searchable datastore. The reference value may be used to access the original eventrecord directly from the field searchable data store.

In one or more embodiments, if one or more of the queries is acollection query, the responsive indexers may generate summarizationinformation based on the fields of the event records located in thefield searchable data store. In at least one of the various embodiments,one or more of the fields used in the summarization information may belisted in the collection query and/or they may be determined based onterms included in the collection query. For example, a collection querymay include an explicit list of fields to summarize. Or, in at least oneof the various embodiments, a collection query may include terms orexpressions that explicitly define the fields, e.g., using regex rules.In FIG. 7C, prior to running the collection query that generates theinverted index 722, the field name “clientip” may need to be defined ina configuration file by specifying the “access_combined” source type anda regular expression rule to parse out the client IP address.Alternatively, the collection query may contain an explicit definitionfor the field name “clientip” which may obviate the need to referencethe configuration file at search time.

In one or more embodiments, collection queries may be saved andscheduled to run periodically. These scheduled collection queries mayperiodically update the summarization information corresponding to thequery. For example, if the collection query that generates invertedindex 722 is scheduled to run periodically, one or more indexers wouldperiodically search through the relevant buckets to update invertedindex 722 with event data for any new events with the “clientip” valueof “127.0.0.1.”

In some embodiments, the inverted indexes that include fields, values,and reference value (e.g., inverted index 722) for event records may beincluded in the summarization information provided to the user. In otherembodiments, a user may not be interested in specific fields and valuescontained in the inverted index, but may need to perform a statisticalquery on the data in the inverted index. For example, referencing theexample of FIG. 7C rather than viewing the fields within summarizationtable 722, a user may want to generate a count of all client requestsfrom IP address “127.0.0.1.” In this case, the search engine wouldsimply return a result of “4” rather than including details about theinverted index 722 in the information provided to the user.

The pipelined search language, e.g., SPL of the SPLUNK® ENTERPRISEsystem may be used to pipe the contents of an inverted index to astatistical query using the “stats” command for example. A “stats” queryrefers to queries that generate result sets that may produce aggregateand statistical results from event records, e.g., average, mean, max,min, rms, etc. Where sufficient information is available in an invertedindex, a “stats” query may generate their result sets rapidly from thesummarization information available in the inverted index rather thandirectly scanning event records. For example, the contents of invertedindex 722 may be pipelined to a stats query, e.g., a “count” functionthat counts the number of entries in the inverted index and returns avalue of “4.” In this way, inverted indexes may enable various statsqueries to be performed absent scanning or search the event records.Accordingly, this optimization technique enables the system to quicklyprocess queries that seek to determine how many events have a particularvalue for a particular field. To this end, the system may examine theentry in the inverted index to count instances of the specific value inthe field without having to go through the individual events or performdata extractions at search time.

In some embodiments, the system maintains a separate inverted index foreach of the above-described time-specific buckets that stores events fora specific time range. A bucket-specific inverted index includes entriesfor specific field-value combinations that occur in events in thespecific bucket. Alternatively, the system may maintain a separateinverted index for each indexer. The indexer-specific inverted indexincludes entries for the events in a data store that are managed by thespecific indexer. Indexer-specific inverted indexes may also bebucket-specific. In at least one or more embodiments, if one or more ofthe queries is a stats query, each indexer may generate a partial resultset from previously generated summarization information. The partialresult sets may be returned to the search head that received the queryand combined into a single result set for the query

As mentioned above, the inverted index may be populated by running aperiodic query that scans a set of events to find instances of aspecific field-value combination, or alternatively instances of allfield-value combinations for a specific field. A periodic query may beinitiated by a user, or may be scheduled to occur automatically atspecific time intervals. A periodic query may also be automaticallylaunched in response to a query that asks for a specific field-valuecombination. In some embodiments, if summarization information is absentfrom an indexer that includes responsive event records, further actionsmay be taken, such as, the summarization information may be generated onthe fly, warnings may be provided the user, the collection queryoperation may be halted, the absence of summarization information may beignored, or the like, or combination thereof.

In one or more embodiments, an inverted index may be set up to updatecontinually. For example, the query may ask for the inverted index toupdate its result periodically, e.g., every hour. In such instances, theinverted index may be a dynamic data structure that is regularly updatedto include information regarding incoming events.

In some cases, e.g., where a query is executed before an inverted indexupdates, when the inverted index may not cover all of the events thatare relevant to a query, the system may use the inverted index to obtainpartial results for the events that are covered by inverted index, butmay also have to search through other events that are not covered by theinverted index to produce additional results on the fly. In other words,an indexer would need to search through event data on the data store tosupplement the partial results. These additional results may then becombined with the partial results to produce a final set of results forthe query. Note that in typical instances where an inverted index is notcompletely up to date, the number of events that an indexer would needto search through to supplement the results from the inverted indexwould be relatively small. In other words, the search to get the mostrecent results may be quick and efficient because only a small number ofevent records will be searched through to supplement the informationfrom the inverted index. The inverted index and associated techniquesare described in more detail in U.S. Pat. No. 8,682,925, entitled“DISTRIBUTED HIGH PERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014,U.S. Pat. No. 9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCEANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO ANEVENT QUERY”, filed on 31 Jan. 2014, and U.S. patent application Ser.No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROL DEVICE”, filed on21 Feb. 2014, each of which is hereby incorporated by reference in itsentirety.

In one or more embodiments, if the system needs to process all eventsthat have a specific field-value combination, the system may use thereferences in the inverted index entry to directly access the events toextract further information without having to search all of the eventsto find the specific field-value combination at search time. In otherwords, the system may use the reference values to locate the associatedevent data in the field searchable data store and extract furtherinformation from those events, e.g., extract further field values fromthe events for purposes of filtering or processing or both.

The information extracted from the event data using the reference valuesmay be directed for further filtering or processing in a query using thepipeline search language. The pipelined search language will, in oneembodiment, include syntax that may direct the initial filtering step ina query to an inverted index. In one embodiment, a user would includesyntax in the query that explicitly directs the initial searching orfiltering step to the inverted index.

Referencing the example in FIG. 7C, if the user determines that sheneeds the user id fields associated with the client requests from IPaddress “127.0.0.1,” instead of incurring the computational overhead ofperforming a brand new search or re-generating the inverted index withan additional field, the user may generate a query that explicitlydirects or pipes the contents of the already generated inverted index722 to another filtering step requesting the user ids for the entries ininverted index 722 where the server response time is greater than“0.0900” microseconds. The search engine would use the reference valuesstored in inverted index 722 to retrieve the event data from the fieldsearchable data store, filter the results based on the “response time”field values and, further, extract the user id field from the resultingevent data to return to the user. In the present instance, the user ids“frank” and “carlos” would be returned to the user from the generatedresults table 722.

In one embodiment, the same methodology may be used to pipe the contentsof the inverted index to a processing step. In other words, the user isable to use the inverted index to efficiently and quickly performaggregate functions on field values that were not part of the initiallygenerated inverted index. For example, a user may want to determine anaverage object size (size of the requested gif) requested by clientsfrom IP address “127.0.0.1.” In this case, the search engine would againuse the reference values stored in inverted index 722 to retrieve theevent data from the field searchable data store and, further, extractthe object size field values from the associated events 731, 732, 733and 734. Once, the corresponding object sizes have been extracted (i.e.2326, 2900, 2920, and 5000), the average may be computed and returned tothe user.

In one embodiment, instead of explicitly invoking the inverted index ina user-generated query, e.g., by the use of special commands or syntax,the SPLUNK® ENTERPRISE system may be configured to automaticallydetermine if any prior-generated inverted index may be used to expeditea user query. For example, the user's query may request the averageobject size (size of the requested gif) requested by clients from IPaddress “127.0.0.1.” without any reference to or use of inverted index722. The search engine, in this case, would automatically determine thatan inverted index 722 already exists in the system that could expeditethis query. In one embodiment, prior to running any search comprising afield-value pair, for example, a search engine may search though all theexisting inverted indexes to determine if a pre-generated inverted indexcould be used to expedite the search comprising the field-value pair.Accordingly, the search engine would automatically use the pre-generatedinverted index, e.g., index 722 to generate the results without anyuser-involvement that directs the use of the index.

Using the reference values in an inverted index to be able to directlyaccess the event data in the field searchable data store and extractfurther information from the associated event data for further filteringand processing is highly advantageous because it avoids incurring thecomputation overhead of regenerating the inverted index with additionalfields or performing a new search.

The data collection, indexing, and visualization system includes one ormore forwarders that receive raw machine data from a variety of inputdata sources, and one or more indexers that process and store the datain one or more data stores. By distributing events among the indexersand data stores, the indexers may analyze events for a query inparallel. In one or more embodiments, a multiple indexer implementationof the search system would maintain a separate and respective invertedindex for each of the above-described time-specific buckets that storesevents for a specific time range. A bucket-specific inverted indexincludes entries for specific field-value combinations that occur inevents in the specific bucket. As explained above, a search head wouldbe able to correlate and synthesize data from across the various bucketsand indexers.

This feature advantageously expedites searches because instead ofperforming a computationally intensive search in a centrally locatedinverted index that catalogues all the relevant events, an indexer isable to directly search an inverted index stored in a bucket associatedwith the time-range specified in the query. This allows the search to beperformed in parallel across the various indexers. Further, if the queryrequests further filtering or processing to be conducted on the eventdata referenced by the locally stored bucket-specific inverted index,the indexer is able to simply access the event records stored in theassociated bucket for further filtering and processing instead ofneeding to access a central repository of event records, which woulddramatically add to the computational overhead.

In one embodiment, there may be multiple buckets associated with thetime-range specified in a query. If the query is directed to an invertedindex, or if the search engine automatically determines that using aninverted index would expedite the processing of the query, the indexerswill search through each of the inverted indexes associated with thebuckets for the specified time-range. This feature allows the HighPerformance Analytics Store to be scaled easily.

In certain instances, where a query is executed before a bucket-specificinverted index updates, when the bucket-specific inverted index may notcover all of the events that are relevant to a query, the system may usethe bucket-specific inverted index to obtain partial results for theevents that are covered by bucket-specific inverted index, but may alsohave to search through the event data in the bucket associated with thebucket-specific inverted index to produce additional results on the fly.In other words, an indexer would need to search through event datastored in the bucket (that was not yet processed by the indexer for thecorresponding inverted index) to supplement the partial results from thebucket-specific inverted index.

FIG. 7D presents a flowchart illustrating how an inverted index in apipelined search query may be used to determine a set of event data thatmay be further limited by filtering or processing in accordance with thedisclosed embodiments.

At block 742, a query is received by a data collection, indexing, andvisualization system. In some embodiments, the query may be received asa user generated query entered into search bar of a graphical usersearch interface. The search interface also includes a time rangecontrol element that enables specification of a time range for thequery.

At block 744, an inverted index is retrieved. Note, that the invertedindex may be retrieved in response to an explicit user search commandinputted as part of the user generated query. Alternatively, the searchengine may be configured to automatically use an inverted index if itdetermines that using the inverted index would expedite the servicing ofthe user generated query. Each of the entries in an inverted index keepstrack of instances of a specific value in a specific field in the eventdata and includes references to events containing the specific value inthe specific field. In order to expedite queries, in most embodiments,the search engine will employ the inverted index separate from the rawrecord data store to generate responses to the received queries.

At block 746, the query engine determines if the query contains furtherfiltering and processing steps. If the query contains no furthercommands, then, in one embodiment, summarization information may beprovided to the user at block 754.

If, however, the query does contain further filtering and processingcommands, then at block 750, the query engine determines if the commandsrelate to further filtering or processing of the data extracted as partof the inverted index or whether the commands are directed to using theinverted index as an initial filtering step to further filter andprocess event data referenced by the entries in the inverted index. Ifthe query may be completed using data already in the generated invertedindex, then the further filtering or processing steps, e.g., a “count”number of records function, “average” number of records per hour etc.are performed and the results are provided to the user at block 752.

If, however, the query references fields that are not extracted in theinverted index, then the indexers will access event data pointed to bythe reference values in the inverted index to retrieve any furtherinformation required at block 756. Subsequently, any further filteringor processing steps are performed on the fields extracted directly fromthe event data and the results are provided to the user at step 758.

In some embodiments, a data server system such as the data collection,indexing, and visualization system may accelerate the process ofperiodically generating updated reports based on query results. Toaccelerate this process, a summarization engine automatically examinesthe query to determine whether generation of updated reports may beaccelerated by creating intermediate summaries. If reports may beaccelerated, the summarization engine periodically generates a summarycovering data obtained during a latest non-overlapping time period. Forexample, where the query seeks events meeting a specified criteria, asummary for the time period includes only events within the time periodthat meet the specified criteria. Similarly, if the query seeksstatistics calculated from the events, such as the number of events thatmatch the specified criteria, then the summary for the time periodincludes the number of events in the period that match the specifiedcriteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query may be run on theseadditional events. Then, the results returned by this query on theadditional events, along with the partial results obtained from theintermediate summaries, may be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries may be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries may save the work involved inre-running the query for previous time periods, so advantageously onlythe newer events needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety for all purposes.

The data collection, indexing, and visualization system provides variousschemas, dashboards, and visualizations that simplify developers' tasksto create applications with additional capabilities. One suchapplication is an enterprise security application, such as SPLUNK®ENTERPRISE SECURITY, which performs monitoring and alerting operationsand includes analytics to facilitate identifying both known and unknownsecurity threats based on large volumes of data stored by the datacollection, indexing, and visualization system. The enterprise securityapplication provides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of the datacollection, indexing, and visualization system searching and reportingcapabilities, the enterprise security application provides a top-downand bottom-up view of an organization's security posture.

The enterprise security application leverages the data collection,indexing, and visualization system search-time normalization techniques,saved searches, and correlation searches to provide visibility intosecurity-relevant threats and activity and generate notable events fortracking. The enterprise security application enables the securitypractitioner to investigate and explore the data to find new or unknownthreats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemslack the infrastructure to effectively store and analyze large volumesof security-related data. Traditional SIEM systems typically use fixedschemas to extract data from pre-defined security-related fields at dataingestion time and store the extracted data in a relational database.This traditional data extraction process (and associated reduction indata size) that occurs at data ingestion time inevitably hampers futureincident investigations that may need original data to determine theroot cause of a security issue, or to detect the onset of an impendingsecurity threat.

In contrast, the enterprise security application system stores largevolumes of minimally-processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the enterprise security application provides pre-specified schemas forextracting relevant values from the different types of security-relatedevents and enables a user to define such schemas.

The enterprise security application may process many types ofsecurity-related information. In general, this security-relatedinformation may include any information that may be used to identifysecurity threats. For example, the security-related information mayinclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. Pat. No. 9,215,240, entitled “INVESTIGATIVE AND DYNAMIC DETECTIONOF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS IN BIG DATA”, issuedon 15 Dec. 2015, U.S. Pat. No. 9,173,801, entitled “GRAPHIC DISPLAY OFSECURITY THREATS BASED ON INDICATIONS OF ACCESS TO NEWLY REGISTEREDDOMAINS”, issued on 3 Nov. 2015, U.S. Pat. No. 9,248,068, entitled“SECURITY THREAT DETECTION OF NEWLY REGISTERED DOMAINS”, issued on 2Feb. 2016, U.S. Pat. No. 9,426,172, entitled “SECURITY THREAT DETECTIONUSING DOMAIN NAME ACCESSES”, issued on 23 Aug. 2016, and U.S. Pat. No.9,432,396, entitled “SECURITY THREAT DETECTION USING DOMAIN NAMEREGISTRATIONS”, issued on 30 Aug. 2016, each of which is herebyincorporated by reference in its entirety for all purposes.Security-related information may also include malware infection data andsystem configuration information, as well as access control information,such as login/logout information and access failure notifications. Thesecurity-related information may originate from various sources within adata center, such as hosts, virtual machines, storage devices andsensors. The security-related information may also originate fromvarious sources in a network, such as routers, switches, email servers,proxy servers, gateways, firewalls and intrusion-detection systems.

During operation, the enterprise security application facilitatesdetecting “notable events” that are likely to indicate a securitythreat. A notable event represents one or more anomalous incidents, theoccurrence of which may be identified based on one or more events (e.g.,time stamped portions of raw machine data) fulfilling pre-specifiedand/or dynamically-determined (e.g., based on machine-learning) criteriadefined for that notable event. Examples of notable events include therepeated occurrence of an abnormal spike in network usage over a periodof time, a single occurrence of unauthorized access to system, a hostcommunicating with a server on a known threat list, and the like. Thesenotable events may be detected in a number of ways, such as: (1) a usermay notice a correlation in events and may manually identify that acorresponding group of one or more events amounts to a notable event; or(2) a user may define a “correlation search” specifying criteria for anotable event, and every time one or more events satisfy the criteria,the application may indicate that the one or more events correspond to anotable event; and the like. A user may alternatively select apre-defined correlation search provided by the application. Note thatcorrelation searches may be run continuously or at regular intervals(e.g., every hour) to search for notable events. Upon detection, notableevents may be stored in a dedicated “notable events index,” which may besubsequently accessed to generate various visualizations containingsecurity-related information. Also, alerts may be generated to notifysystem operators when important notable events are discovered.

The enterprise security application provides various visualizations toaid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 17A illustrates anexample key indicators view 1700 that comprises a dashboard, which maydisplay a value 1701, for various security-related metrics, such asmalware infections 1702. It may also display a change in a metric value1703, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 1700 additionallydisplays a histogram panel 1704 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations may also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents may include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 17B illustrates an example incident review dashboard 1710 thatincludes a set of incident attribute fields 1711 that, for example,enables a user to specify a time range field 1712 for the displayedevents. It also includes a timeline 1713 that graphically illustratesthe number of incidents that occurred in time intervals over theselected time range. It additionally displays an events list 1714 thatenables a user to view a list of all of the notable events that matchthe criteria in the incident attributes fields 1711. To facilitateidentifying patterns among the notable events, each notable event may beassociated with an urgency value (e.g., low, medium, high, critical),which is indicated in the incident review dashboard. The urgency valuefor a detected event may be determined based on the severity of theevent and the priority of the system component associated with theevent.

As mentioned above, the data intake and query platform provides variousfeatures that simplify the developer's task to create variousapplications. One such application is a virtual machine monitoringapplication, such as SPLUNK® APP FOR VMWARE® that provides operationalvisibility into granular performance metrics, logs, tasks and events,and topology from hosts, virtual machines and virtual centers. Itempowers administrators with an accurate real-time picture of the healthof the environment, proactively identifying performance and capacitybottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the virtual machine monitoring application stores largevolumes of minimally processed machine data, such as performanceinformation and log data, at ingestion time for later retrieval andanalysis at search time when a live performance issue is beinginvestigated. In addition to data obtained from various log files, thisperformance-related information may include values for performancemetrics obtained through an application programming interface (API)provided as part of the vSphere Hypervisor™ system distributed byVMware, Inc. of Palo Alto, Calif. For example, these performance metricsmay include: (1) CPU-related performance metrics; (2) disk-relatedperformance metrics; (3) memory-related performance metrics; (4)network-related performance metrics; (5) energy-usage statistics; (6)data-traffic-related performance metrics; (7) overall systemavailability performance metrics; (8) cluster-related performancemetrics; and (9) virtual machine performance statistics. Suchperformance metrics are described in U.S. patent application Ser. No.14/167,316, entitled “CORRELATION FOR USER-SELECTED TIME RANGES OFVALUES FOR PERFORMANCE METRICS OF COMPONENTS IN ANINFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROM THATINFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, and which ishereby incorporated by reference in its entirety for all purposes.

To facilitate retrieving information of interest from performance dataand log files, the virtual machine monitoring application providespre-specified schemas for extracting relevant values from differenttypes of performance-related events, and also enables a user to definesuch schemas.

The virtual machine monitoring application additionally provides variousvisualizations to facilitate detecting and diagnosing the root cause ofperformance problems. For example, one such visualization is a“proactive monitoring tree” that enables a user to easily view andunderstand relationships among various factors that affect theperformance of a hierarchically structured computing system. Thisproactive monitoring tree enables a user to easily navigate thehierarchy by selectively expanding nodes representing various entities(e.g., virtual centers or computing clusters) to view performanceinformation for lower-level nodes associated with lower-level entities(e.g., virtual machines or host systems). Example node-expansionoperations are illustrated in FIG. 17C, wherein nodes 1733 and 1734 areselectively expanded. Note that nodes 1731-1739 may be displayed usingdifferent patterns or colors to represent different performance states,such as a critical state, a warning state, a normal state or anunknown/offline state. The ease of navigation provided by selectiveexpansion in combination with the associated performance-stateinformation enables a user to quickly diagnose the root cause of aperformance problem. The proactive monitoring tree is described infurther detail in U.S. Pat. No. 9,185,007, entitled “PROACTIVEMONITORING TREE WITH SEVERITY STATE SORTING”, issued on 10 Nov. 2015,and U.S. Pat. No. 9,426,045, also entitled “PROACTIVE MONITORING TREEWITH SEVERITY STATE SORTING”, issued on 23 Aug. 2016, each of which ishereby incorporated by reference in its entirety for all purposes.

The virtual machine monitoring application also provides a userinterface that enables a user to select a specific time range and thenview heterogeneous data comprising events, log data, and associatedperformance metrics for the selected time range. For example, the screenillustrated in FIG. 17D displays a listing of recent “tasks and events”and a listing of recent “log entries” for a selected time range above aperformance-metric graph for “average CPU core utilization” for theselected time range. Note that a user is able to operate pull-down menus1742 to selectively display different performance metric graphs for theselected time range. This enables the user to correlate trends in theperformance-metric graph with corresponding event and log data toquickly determine the root cause of a performance problem. This userinterface is described in more detail in U.S. patent application Ser.No. 14/167,316, entitled “CORRELATION FOR USER-SELECTED TIME RANGES OFVALUES FOR PERFORMANCE METRICS OF COMPONENTS IN ANINFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROM THATINFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, and which ishereby incorporated by reference in its entirety for all purposes.

As previously mentioned, the data intake and query platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is an IT monitoring application, such as SPLUNK® ITSERVICE INTELLIGENCE™, which performs monitoring and alertingoperations. The IT monitoring application also includes analytics tohelp an analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the data collection, indexing, andvisualization system as correlated to the various services an ITorganization provides (a service-centric view). This differssignificantly from conventional IT monitoring systems that lack theinfrastructure to effectively store and analyze large volumes ofservice-related events. Traditional service monitoring systems typicallyuse fixed schemas to extract data from pre-defined fields at dataingestion time, wherein the extracted data is typically stored in arelational database. This data extraction process and associatedreduction in data content that occurs at data ingestion time inevitablyhampers future investigations, when all of the original data may beneeded to determine the root cause of or contributing factors to aservice issue.

In contrast, an IT monitoring application system stores large volumes ofminimally-processed service-related data at ingestion time for laterretrieval and analysis at search time, to perform regular monitoring, orto investigate a service issue. To facilitate this data retrievalprocess, the IT monitoring application enables a user to define an IToperations infrastructure from the perspective of the services itprovides. In this service-centric approach, a service such as corporatee-mail may be defined in terms of the entities employed to provide theservice, such as host machines and network devices. Each entity isdefined to include information for identifying all of the events thatpertains to the entity, whether produced by the entity itself or byanother machine, and considering the many various ways the entity may beidentified in machine data (such as by a URL, an IP address, or machinename). The service and entity definitions may organize events around aservice so that all of the events pertaining to that service may beeasily identified. This capability provides a foundation for theimplementation of Key Performance Indicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the IT monitoring application. Each KPI measures an aspect ofservice performance at a point in time or over a period of time (aspectKPI's). Each KPI is defined by a search query that derives a KPI valuefrom the machine data of events associated with the entities thatprovide the service. Information in the entity definitions may be usedto identify the appropriate events at the time a KPI is defined orwhenever a KPI value is being determined. The KPI values derived overtime may be stored to build a valuable repository of current andhistorical performance information for the service, and the repository,itself, may be subject to search query processing. Aggregate KPIs may bedefined to provide a measure of service performance calculated from aset of service aspect KPI values; this aggregate may even be takenacross defined timeframes and/or across multiple services. A particularservice may have an aggregate KPI derived from substantially all of theaspect KPI's of the service to indicate an overall health score for theservice.

The IT monitoring application facilitates the production of meaningfulaggregate KPI's through a system of KPI thresholds and state values.Different KPI definitions may produce values in different ranges, and sothe same value may mean something very different from one KPI definitionto another. To address this, the IT monitoring application implements atranslation of individual KPI values to a common domain of “state”values. For example, a KPI range of values may be 1-100, or 50-275,while values in the state domain may be ‘critical,’ warning,′ ‘normal,’and ‘informational’. Thresholds associated with a particular KPIdefinition determine ranges of values for that KPI that correspond tothe various state values. In one case, KPI values 95-100 may be set tocorrespond to ‘critical’ in the state domain. KPI values from disparateKPI's may be processed uniformly once they are translated into thecommon state values using the thresholds. For example, “normal 80% ofthe time” may be applied across various KPI's. To provide meaningfulaggregate KPI's, a weighting value may be assigned to each KPI so thatits influence on the calculated aggregate KPI value is increased ordecreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. The IT monitoring application may reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service may be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in the IT monitoring application may includeinformational fields that may serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in the IT monitoring applicationmay also be created and updated by an import of tabular data (asrepresented in a CSV, another delimited file, or a search query resultset). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in the IT monitoring application may also be associated witha service by means of a service definition rule. Processing the ruleresults in the matching entity definitions being associated with theservice definition. The rule may be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, the IT monitoring application may recognize notableevents that may indicate a service performance problem or othersituation of interest. These notable events may be recognized by a“correlation search” specifying trigger criteria for a notable event:every time KPI values satisfy the criteria, the application indicates anotable event. A severity level for the notable event may also bespecified. Furthermore, when trigger criteria are satisfied, thecorrelation search may additionally or alternatively cause a serviceticket to be created in an IT service management (ITSM) system, such asa systems available from ServiceNow, Inc., of Santa Clara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of events and the KPI valuesgenerated and collected. Visualizations may be particularly useful formonitoring or investigating service performance. The IT monitoringapplication provides a service monitoring interface suitable as the homepage for ongoing IT service monitoring. The interface is appropriate forsettings such as desktop use or for a wall-mounted display in a networkoperations center (NOC). The interface may prominently display aservices health section with tiles for the aggregate KPI's indicatingoverall health for defined services and a general KPI section with tilesfor KPI's related to individual service aspects. These tiles may displayKPI information in a variety of ways, such as by being colored andordered according to factors like the KPI state value. They also may beinteractive and navigate to visualizations of more detailed KPIinformation.

The IT monitoring application provides a service-monitoring dashboardvisualization based on a user-defined template. The template may includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets may responddynamically to changing KPI information. The KPI widgets may appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements may be interactive so as toprovide navigation to visualizations of more detailed KPI information.

The IT monitoring application provides a visualization showing detailedtime-series information for multiple KPI's in parallel graph lanes. Thelength of each lane may correspond to a uniform time range, while thewidth of each lane may be automatically adjusted to fit the displayedKPI data. Data within each lane may be displayed in a user selectablestyle, such as a line, area, or bar chart. During operation a user mayselect a position in the time range of the graph lanes to activate laneinspection at that point in time. Lane inspection may display anindicator for the selected time across the graph lanes and display theKPI value associated with that point in time for each of the graphlanes. The visualization may also provide navigation to an interface fordefining a correlation search, using information from the visualizationto pre-populate the definition.

The IT monitoring application provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event. The IT monitoring applicationprovides pre-specified schemas for extracting relevant values from thedifferent types of service-related events. It also enables a user todefine such schemas.

As noted herein above, the data collection, indexing, and visualizationsystem operating in accordance with aspects of the present disclosuremay employ one or more data ingestion components (e.g., forwarders,connectors, ingest services, collect services, REST APIs, etc.) in orderto ingest raw machine data from one or more sources. The ingested datastreams may be processed by a data processing pipeline, which maytransform the data stream and feed the transformed data stream to one ormore indexers of the data collection, indexing, and visualization systemfor further processing.

FIG. 18 schematically illustrates an example data stream processingarchitecture implemented by data collection, indexing, and visualizationsystems operating in accordance with aspects of the present disclosure.As schematically illustrated by FIG. 18, the Data Stream Processor 1800may perform real-time data stream processing by the data processingpipeline 1805 implementing a series of data processing functions(“pipeline stages”) 1810A-1810N which define transformations of dataflowing from one or more data sources to one or more data sinks.

In various illustrative examples, the data transformation operationsimplemented by the data processing pipeline 1805 may include dataaggregation (aggregating data based on specific conditions), dataformatting (formatting data based on specified conditions), data routing(delivering data to one or more destinations based on specifiedconditions), and/or data filtering (filtering or routing noisy data toone or more destinations based on specified conditions), as described inmore detail herein below with reference to FIG. 19.

As schematically illustrated by FIG. 18, the firehose stage 1810Areceives the input data stream from one or more data sources (e.g.,ingest service 1815, forwarder service 1818, and collect service 1820)and feeds the subsequent pipeline stages 1810, which can perform variousdata processing and transformation operations (e.g., line breaking,timestamp extraction, and/or field extraction), as described in moredetail herein below with reference to FIG. 19. The final stage of thepipeline 1805 is the data sink 1810N, which feeds the transformed datastream produced by the data processing pipeline 1805 to one or moreindexers 1825A-1825L and/or other components of the data collection,indexing, and visualization system, as well as to one or more thirdparty services 1828.

The example data stream processing architecture 1800 may further employone or more pull-based connectors 1830 for receiving data from one ormore external data sources 1850. The example data stream processingarchitecture 1800 may further employ one or more push-based connectors1835A-1835M for receiving data from one or more external data sources1850.

FIG. 19 schematically illustrates an example data processing pipeline1900 implemented by the data stream processing operating in accordancewith aspects of the present disclosure. As schematically illustrated byFIG. 19, the input data stream 1905 sequentially flows through thefollowing pipeline stages: receive from forwarder stage 1910, apply linebreak stage 1920, apply timestamp extraction stage 1930, apply fieldextraction stage 1940, and send to indexer stage 1950. Accordingly, eachchunk of raw machine data that comes into the pipeline 1900 issequentially processed by the pipeline stages 1910, 1920, 1930, 1940,and 1940, and each pipeline stage sequentially processes series ofchunks of data which it receives from the previous pipeline stage andforwards, upon completing its data processing operations, to the nextpipeline stage. While the example of FIG. 19 describes particularpipeline stages arranged in a certain order, various otherimplementations may implement different functional designations and/ororder of the pipeline stages. In some implementations, the dataprocessing pipeline may comprise one or more custom pipeline stages forperforming certain application-specific functionality.

The data stream processor may employ one or more sets of rules forimplementing the data processing pipeline 1900. Each set of rules mayinclude one or more rules, and each rule may specify an action to beperformed responsive to successfully matching a specified pattern to oneor more points of the input data stream that is fed to the pipelinestage. The result of performing the action on the matching data pointsforms the output data stream of the pipeline stage. The action performedby the rule may be specified by the “kind” rule attribute, which may bespecified for one or more rules that are grouped together into a set ofrules. In some implementations, the actions that may be invoked by agiven rule may be restricted based on the value of the “kind” attributeof the rule, thus implementing security and/or performance constraints.

Furthermore, each rule may be characterized by the “source type”attribute, which may specify the source type to be matched to the inputdata stream. The source type of the input data stream may the reflectvendor, platform, and/or technology associated with the externalsystem(s) that generated the input data stream. The source type may bedetermined by applying, to one or more data points of the input datastream, special rules that implement keyword matching, pattern matching,and/or machine learning-based classifiers in order to associate theinput data stream with a particular source type.

In some implementations, a separate ruleset may be associated with eachsource type. Accordingly, should the data stream processor identify twoor more rulesets associated with the same source type, a conflictresolution procedure may be performed. In various illustrative examples,the conflict resolution procedure may involve selecting, among two ormore conflicting rulesets (i.e., two or more rulesets having the samesource type attribute), the ruleset having the most recent timestamp,the ruleset having the larger version number, or the first ruleset in alexicographically ordered sequence of identifiers of the conflictingrulesets.

Alternatively, a single ruleset may comprise rules associated withdifferent source types, and may be grouped by the source type.Accordingly, the conflict resolution procedure may involve selecting,among two or more conflicting rules (i.e., two or more rules having thesame kind attribute and the same source type attribute), the rule havingthe larger version number and/or the first rule in a lexicographicallyordered sequence of identifiers of the conflicting rules.

In some implementations, if two or more source type definitions matchone or more data points of the input data stream, these data points maybe copied, such that a separate copy is created for each matchingdefinition, and the subsequent pipeline stages are performed on eachcopy of the data points by invoking the rules having their source typeattribute matching the source type associated with the copy of thesubset of the input data stream. This way, the data processing pipelinemay be effectively branched for at least a subset of the input datastream.

Therefore, maintaining single source type rulesets may simplify addingnew source types, modifying existing source types, and/or removingsource types. On the other hand, maintaining a single ruleset for allsource types may simplify debugging and version control.

FIG. 20 schematically illustrates an example structure of a rulesetemployed by the data stream processing for implementing a dataprocessing pipeline in accordance with aspects of the presentdisclosure. As schematically illustrated by FIG. 20, the example ruleset2000 comprises a plurality of groups 2010A-2010N of rules 2015, suchthat each group 2010 comprises one or more rules 2015A-2015K sharing thesource type attribute 2020 associated with the group. In an illustrativeexample, the ruleset 2000 may be represented by a JSON file. In variousother implementations, other scripting languages may be used forencoding the rulesets.

Each rule 2015 may have the following attributes: the source typeattribute 2022 that should be matched to the source type of the inputdata stream, the kind attribute 2024 that may identify the action thatis performed by the rule (e.g., line break, timestamp extraction, fieldextraction, etc.), the pattern attribute 2024, and the action attribute2028 specifying the actions to be performed on one or more chunks of theinput data stream that match the pattern specified by the patternattribute 2026. In some implementations, the pattern attribute 2024 maybe specified by a regular expression (regex). In some implementations,the action attribute 2026 may specify multiple sub-attributes thatindicate specific parameters of the action, e.g. a lookup table name.

Referring again to FIG. 19, in order to implement a pipeline stage ofthe data processing pipeline 1900, the data stream processor may issue afunction call specifying the value of the kind attribute of the rule(s)to be applied to the input data of the pipeline stage, such that thevalue of the kind attribute matches the function of the pipeline stage(e.g., line break, timestamp extraction, field extraction, etc.).Accordingly, in order to execute the function call, the data streamprocessor identifies, among the rules of the specified kind, one or morerules whose source type attribute matches the source type of the inputdata stream.

As schematically illustrated by FIG. 19, the receive from forwarderstage 1910 receives the input data stream 1905 from a data source (notshown in FIG. 19) and feeds the received input data stream 1910 to theapply line break stage 1920. The apply line break stage 1920 invokes therules having the kind attribute value of “apply line break” and thesource type attribute value matching the source type of the input data.

In an illustrative example, the line break rule may specify the linebreaking pattern, which may be matched to the data points of the inputdata stream. Accordingly, the data stream processor selects, among therules having the kind attribute value of “apply line break,” one or morerules having the source type attribute matching the source type of theinput data. Responsive to successfully matching, to a data point, theline breaking pattern specified by the selected rule, the data streamprocessor performs the action specified by the selected rule on thematched data points (e.g., replacing the matched patterns with apredefined sequence of one or more symbols that encode the line break inthe data collection, indexing, and visualization system).

The apply timestamp extraction stage 1930 receives the data stream fromthe apply line break stage 1920 and invokes the rules having the kindattribute value of “timestamp extraction” and the source type attributevalue matching the source type of the input data. In variousillustrative examples, the timestamp extraction rule may specify thetime format, the time prefix, the timezone identifier, the validdate/time range, and/or various other parameters, which may be specifiedby patterns, regular expressions, character sequences, numeric values,etc. Accordingly, the data stream processor selects, among the ruleshaving the kind attribute value of “timestamp extraction,” one or morerules having the source type attribute matching the source type of theinput data. The selected rule would then specify the pattern to beapplied to the input data in order to perform timestamp extraction.Responsive to successfully matching one or more specified patterns, thedata stream processor performs the action specified by the selected ruleon the matched data points (e.g., assigning the extracted time to thetimestamp attribute of one or more events for later use by the datacollection, indexing, and visualization system).

The apply field extraction stage 1940 receives the data stream from theapply timestamp extraction stage 1930 and invokes the rules having thekind attribute value of “field extraction” and the source type attributevalue matching the source type of the input data. In an illustrativeexample, the rules performing field extraction may specify one or moreregular expressions, conditional statements, computational steps, and/orother operations depending upon the type of the field to be extracted.Accordingly, the data stream processor selects, among the rules havingthe kind attribute value of “field extraction,” one or more rules havingthe source type attribute matching the source type of the input data.The data stream processor then evaluates the condition and performs theaction specified by the selected rule (e.g., assigning the extractedfield values to respective key-value pairs for later use by the datacollection, indexing, and visualization system).

The send to indexer stage 1950 receives the data stream from the applyfield extraction stage 1940 and feeds the output data stream 1955 to oneor more indexers of the data collection, indexing, and visualizationsystem and/or to one or more third party systems for further processing.Thus, employing the source type-specific rulesets allows utilizing asingle data processing pipeline 1900 for processing data from multipledifferent data sources, rather than maintaining multiple data processingpipelines or multiple branches in a single data processing pipeline.

In some implementations, the data stream processor may implement adebugging/monitoring graphical user interface (GUI) for the dataprocessing pipeline. In particular, the data stream processor mayvisually render via the GUI representations of the sequential pipelinestages (e.g., in a manner similar to the depiction of the examplepipeline 1900 in FIG. 19). Responsive to receiving, via the GUI,selection of a particular pipeline stage, the data stream processor mayvisually render via the GUI the source type of the input data stream andan identifier of the rule applied by the pipeline stage in a visualassociation with at least part of the input data stream comprising oneor more data points that match the pattern specified by the rule andcorresponding transformed data points that are outputted by the pipelinestage.

FIG. 21 is a flow diagram of an embodiment of a method 2100 ofrule-based data stream processing implemented by a data stream processoroperating in accordance with aspects of the present disclosure. Method2100 and/or each of its individual functions, routines, subroutines, oroperations may be performed by one or more processors of a computingdevice implementing the data stream processor of the data collection,indexing, and visualization system 108. In certain implementations,method 2100 may be performed by a single processing thread.Alternatively, method 2100 may be performed by two or more processingthreads, each thread executing one or more individual functions,routines, subroutines, or operations of the method. In an illustrativeexample, the processing threads implementing method 2100 may besynchronized (e.g., using semaphores, critical sections, and/or otherthread synchronization mechanisms). Alternatively, the processingthreads implementing method 2100 may be executed asynchronously withrespect to each other. Therefore, while FIG. 21 and the associateddescription lists the operations of method 2100 in certain order,various implementations of the method may perform at least some of thedescribed operations in parallel and/or in arbitrary selected orders.

At block 2110, the computing device implementing the data streamprocessor receives one or more data points of an input data stream. Inan illustrative example, the data points may reflect performancemeasurements of an external system or environment that are associatedwith successive points in time. The data points may represent events,i.e., changes of the state of the external system or environment, asdescribed in more detail herein above.

At block 2120, the computing device processes the raw machine data by adata processing pipeline and produces transformed machine data. The dataprocessing pipeline may comprise an ordered plurality of pipelinestages. A pipeline stage may apply one or more rules to one or more datapoints of its input data stream. Each rule may specify an action to beperformed on the one or more data points responsive to evaluating aconditional expression applied to the input of the pipeline stage. Theconditional expression may specify a pattern to be matched to the one ormore data points. The rule may be selected by matching its source typeattribute to the source type associated with the input data stream. Thesource type may reflect the vendor, the platform, and/or the technologyassociated with the input data stream.

In various illustrative examples, the data processing pipeline maycomprise a receiving pipeline stage that receives data from one or moreforwarders of the collection, indexing, and visualization system. Thedata processing pipeline may further comprise a line breaking pipelinestage, which employs one or more rules specifying line breaking patternsto be matched to the data points of the input data stream. The dataprocessing pipeline may further comprise a timestamp extraction pipelinestage, which employs one or more timestamp extraction rules specifyingthe time format, the time prefix, the timezone identifier, the validdate/time range, and/or various other parameters, and assigns theextracted time to the timestamp attribute of one or more events forlater use by the data collection, indexing, and visualization system.The data processing pipeline may further comprise a field extractionpipeline stage, which extracts one or more values specified by thepertinent rules and assigns the extracted field values to respectivekey-value pairs for later use by the data collection, indexing, andvisualization system. The data processing pipeline may further comprisea final pipeline stage that feeds an output of the data processingpipeline to one or more indexers of the data collection, indexing, andvisualization system and/or to one or more third party systems, asdescribed in more detail herein above.

At block 2130, the computing device feeds the transformed machine dataproduced by the data processing pipeline to a data collection, indexing,and visualization system and/or to one or more third party systems, asdescribed in more detail herein above.

FIG. 22 is a flow diagram of an embodiment of a method 2200 ofimplementing a pipeline stage by a data stream processor operating inaccordance with aspects of the present disclosure. Method 2200 and/oreach of its individual functions, routines, subroutines, or operationsmay be performed by one or more processors of a computing deviceimplementing the data stream processor of the data collection, indexing,and visualization system 108. In certain implementations, method 2200may be performed by a single processing thread. Alternatively, method2200 may be performed by two or more processing threads, each threadexecuting one or more individual functions, routines, subroutines, oroperations of the method. In an illustrative example, the processingthreads implementing method 2200 may be synchronized (e.g., usingsemaphores, critical sections, and/or other thread synchronizationmechanisms). Alternatively, the processing threads implementing method2200 may be executed asynchronously with respect to each other.Therefore, while FIG. 22 and the associated description lists theoperations of method 2200 in certain order, various implementations ofthe method may perform at least some of the described operations inparallel and/or in arbitrary selected orders.

At block 2210, the computing device implementing the pipeline stagereceives the input data stream comprising one or more sequential datapoints, as described in more detail herein above.

At block 2220, the computing device determines the source typeassociated with the input data stream. The source type may be determinedby applying, to one or more data points of the input data stream,special rules that implement keyword matching, pattern matching, and/ormachine learning-based classifiers in order to associate the input datastream with a particular source type, as described in more detail hereinabove.

At block 2230, the computing device selects, among available rules(which may be grouped into one or more rulesets), one or more rules thathave their respective kind attributes matching the action to beperformed by the pipeline stage, and further have their respectivesource type attributes matching the source type of the input datastream.

Should the data stream processor identify two or more conflicting rulesassociated with the source type of the input data stream, a conflictresolution procedure may be performed. In various illustrative examples,the data stream processor may select, among the identified conflictingrules (i.e., two or more rulesets having the same source typeattribute), the rule having the most recent timestamp, the rule havingthe larger version number, or the first rule in a lexicographicallyordered sequence of identifiers of the conflicting rules. The ruleselected by the conflict resolution procedure can be applied by thepipeline stage. The selected rule may specify an action to be performedon a data point responsive to successfully matching, to the data point,a pattern specified by the rule, as described in more detail hereinabove.

Responsive to successfully matching, at block 2240, the patternspecified by the selected rule to one or more data points, the computingdevice, at block 2250, performs the action specified by the selectedrule on the matched data points, as described in more detail hereinabove.

At block 2260, the computing device feeds the transformed data stream tothe next pipeline stage of the data processing pipeline, as described inmore detail herein above.

Computer programs typically comprise one or more instructions set atvarious times in various memory devices of a computing device, which,when read and executed by at least one processor, will cause a computingdevice to execute functions involving the disclosed techniques. In someembodiments, a carrier containing the aforementioned computer programproduct is provided. The carrier is one of an electronic signal, anoptical signal, a radio signal, or a non-transitory computer-readablestorage medium.

Any or all of the features and functions described above may be combinedwith each other, except to the extent it may be otherwise stated aboveor to the extent that any such embodiments may be incompatible by virtueof their function or structure, as will be apparent to persons ofordinary skill in the art. Unless contrary to physical possibility, itis envisioned that (i) the methods/steps described herein may beperformed in any sequence and/or in any combination, and (ii) thecomponents of respective embodiments may be combined in any manner.

Although the subject matter has been described in language specific tostructural features and/or acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as examples of implementing theclaims, and other equivalent features and acts are intended to be withinthe scope of the claims.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense, i.e., in the sense of “including, but notlimited to.” As used herein, the terms “connected,” “coupled,” or anyvariant thereof means any connection or coupling, either direct orindirect, between two or more elements; the coupling or connectionbetween the elements may be physical, logical, or a combination thereof.Additionally, the words “herein,” “above,” “below,” and words of similarimport, when used in this application, refer to this application as awhole and not to any particular portions of this application. Where thecontext permits, words using the singular or plural number may alsoinclude the plural or singular number respectively. The word “or” inreference to a list of two or more items, covers all of the followinginterpretations of the word: any one of the items in the list, all ofthe items in the list, and any combination of the items in the list.Likewise, the term “and/or” in reference to a list of two or more items,covers all of the following interpretations of the word: any one of theitems in the list, all of the items in the list, and any combination ofthe items in the list.

Conjunctive language such as the phrase “at least one of X, Y and Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to convey that an item, term, etc. may beeither X, Y or Z, or any combination thereof. Thus, such conjunctivelanguage is not generally intended to imply that certain embodimentsrequire at least one of X, at least one of Y and at least one of Z toeach be present. Further, use of the phrase “at least one of X, Y or Z”as used in general is to convey that an item, term, etc. may be eitherX, Y or Z, or any combination thereof.

In some embodiments, certain operations, acts, events, or functions ofany of the algorithms described herein may be performed in a differentsequence, may be added, merged, or left out altogether (e.g., not allare necessary for the practice of the algorithms). In certainembodiments, operations, acts, functions, or events may be performedconcurrently, e.g., through multi-threaded processing, interruptprocessing, or multiple processors or processor cores or on otherparallel architectures, rather than sequentially.

Systems and modules described herein may comprise software, firmware,hardware, or any combination(s) of software, firmware, or hardwaresuitable for the purposes described. Software and other modules mayreside and execute on servers, workstations, personal computers,computerized tablets, PDAs, and other computing devices suitable for thepurposes described herein. Software and other modules may be accessiblevia local computer memory, via a network, via a browser, or via othermeans suitable for the purposes described herein. Data structuresdescribed herein may comprise computer files, variables, programmingarrays, programming structures, or any electronic information storageschemes or methods, or any combinations thereof, suitable for thepurposes described herein. User interface elements described herein maycomprise elements from graphical user interfaces, interactive voiceresponse, command line interfaces, and other suitable interfaces.

Further, processing of the various components of the illustrated systemsmay be distributed across multiple machines, networks, and othercomputing resources. In certain embodiments, one or more of thecomponents of the data collection, indexing, and visualization system108 may be implemented in a remote distributed computing system. In thiscontext, a remote distributed computing system or cloud-based servicemay refer to a service hosted by one more computing resources that areaccessible to end users over a network, for example, by using a webbrowser or other application on a client device to interface with theremote computing resources. For example, a service provider may providea data collection, indexing, and visualization system 108 by managingcomputing resources configured to implement various aspects of thesystem (e.g., search head 210, indexers 206, etc.) and by providingaccess to the system to end users via a network.

When implemented as a cloud-based service, various components of thesystem 108 may be implemented using containerization oroperating-system-level virtualization, or other virtualizationtechnique. For example, one or more components of the system 108 (e.g.,search head 210, indexers 206, etc.) may be implemented as separatesoftware containers or container instances. Each container instance mayhave certain resources (e.g., memory, processor, etc.) of the underlyinghost computing system assigned to it, but may share the same operatingsystem and may use the operating system's system call interface. Eachcontainer may provide an isolated execution environment on the hostsystem, such as by providing a memory space of the host system that islogically isolated from memory space of other containers. Further, eachcontainer may run the same or different computer applicationsconcurrently or separately, and may interact with each other. Althoughreference is made herein to containerization and container instances, itwill be understood that other virtualization techniques may be used. Forexample, the components may be implemented using virtual machines usingfull virtualization or paravirtualization, etc. Thus, where reference ismade to “containerized” components, it should be understood that suchcomponents may additionally or alternatively be implemented in otherisolated execution environments, such as a virtual machine environment.

Likewise, the data repositories shown may represent physical and/orlogical data storage, including, e.g., storage area networks or otherdistributed storage systems. Moreover, in some embodiments theconnections between the components shown represent possible paths ofdata flow, rather than actual connections between hardware. While someexamples of possible connections are shown, any of the subset of thecomponents shown may communicate with any other subset of components invarious implementations.

Embodiments are also described above with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products. Each block of the flow chart illustrationsand/or block diagrams, and combinations of blocks in the flow chartillustrations and/or block diagrams, may be implemented by computerprogram instructions. Such instructions may be provided to a processorof a general purpose computer, special purpose computer,specially-equipped computer (e.g., comprising a high-performancedatabase server, a graphics subsystem, etc.) or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor(s) of the computer or other programmabledata processing apparatus, create means for implementing the actsspecified in the flow chart and/or block diagram block or blocks. Thesecomputer program instructions may also be stored in a non-transitorycomputer-readable memory that may direct a computer or otherprogrammable data processing apparatus to operate in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the acts specified in the flow chart and/or blockdiagram block or blocks. The computer program instructions may also beloaded to a computing device or other programmable data processingapparatus to cause operations to be performed on the computing device orother programmable apparatus to produce a computer implemented processsuch that the instructions which execute on the computing device orother programmable apparatus provide steps for implementing the actsspecified in the flow chart and/or block diagram block or blocks.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the invention may be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further implementations of theinvention. These and other changes may be made to the invention in lightof the above Detailed Description. While the above description describescertain examples of the invention, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention may be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the invention disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the invention under theclaims.

To reduce the number of claims, certain aspects of the invention arepresented below in certain claim forms, but the applicant contemplatesother aspects of the invention in any number of claim forms. Forexample, while only one aspect of the invention is recited as ameans-plus-function claim under 35 U.S.C. sec. 112(f) (AIA), otheraspects may likewise be embodied as a means-plus-function claim, or inother forms, such as being embodied in a computer-readable medium. Anyclaims intended to be treated under 35 U.S.C. § 112(f) will begin withthe words “means for,” but use of the term “for” in any other context isnot intended to invoke treatment under 35 U. S.C. § 112(f). Accordingly,the applicant reserves the right to pursue additional claims afterfiling this application, in either this application or in a continuingapplication.

What is claimed is:
 1. A method implemented by one or more processingdevices of a computer system, the method comprising: receiving, by thecomputer system, an input data stream comprising raw machine data;processing the raw machine data by a data processing pipeline thatproduces transformed machine data, wherein the data processing pipelinecomprises an ordered plurality of pipeline stages, wherein a pipelinestage of the ordered plurality of pipeline stages applies a rule of aset of rules to an input of the pipeline stage, wherein the rulespecifies an action to be performed on the input of the pipeline stageresponsive to evaluating a conditional expression applied to the inputof the pipeline stage, wherein the action generates an output of thepipeline stage, and wherein the rule is selected based on a source typeassociated with the input data stream; and supplying the transformedmachine data to a data collection, indexing, and visualization system.2. The method of claim 1, wherein the source type reflects at least oneof: a vendor, a platform, or a technology associated with the input datastream.
 3. The method of claim 1, wherein the data processing pipelinecomprises a receiving pipeline stage that receives data from one or moreforwarders.
 4. The method of claim 1, wherein the data processingpipeline comprises a line breaking pipeline stage.
 5. The method ofclaim 1, wherein the data processing pipeline comprises a timestampextraction pipeline stage.
 6. The method of claim 1, wherein the dataprocessing pipeline comprises a filed extraction pipeline stage.
 7. Themethod of claim 1, wherein the data processing pipeline comprises afinal pipeline stage that feeds an output of the data processingpipeline to one or more indexers of the data collection, indexing, andvisualization system.
 8. The method of claim 1, wherein the dataprocessing pipeline comprises a final pipeline stage that feeds anoutput of the data processing pipeline to one or more third partysystems.
 9. The method of claim 1, wherein each rule of the rule set isassociated with a kind attribute specifying the action generating theoutput of the pipeline stage.
 10. The method of claim 1, furthercomprising: causing at least part of the input of the pipeline stage andat least part of the output of the pipeline stage to be visuallyrendered via a graphical user interface in a visual association with thesource type and an identifier of the rule that is applied by thepipeline stage.
 11. The method of claim 1, wherein the conditionalexpression specifies a pattern to be matched to the input of thepipeline stage.
 12. The method of claim 1, wherein the set of rules isrepresented by a script in a predefined scripting language.
 13. Themethod of claim 1, wherein each rule of the set of rules is associatedwith a source type attribute, and wherein rules of the set of rules aregrouped by the source type attribute.
 14. The method of claim 1, whereinthe raw machine data comprises a plurality of data points representingevents, wherein each event reflects a change of state of an externalsystem.
 15. The method of claim 1, wherein processing the raw machinedata further comprises: determining the source type associated with theinput data stream, by applying one or more special rules that implementat least one of: keyword matching or pattern matching in order toassociate the input data stream with a particular source type.
 16. Themethod of claim 1, wherein processing the raw machine data furthercomprises: determining the source type associated with the input datastream, by applying one or more special rules that implement a trainableclassifier in order to associate the input data stream with a particularsource type.
 17. The method of claim 1, further comprising: responsiveto determining that two or more source type definitions match one ormore data points of the input data stream, creating two or more copiesof the one or more data points; and performing one or more subsequentpipeline stages on each copy of the two or more copies of the one ormore data points, by invoking rules having their source type attributesmatching respective source types associated with the two or more copiesof the one or more data points.
 18. The method of claim 1, furthercomprising: responsive to determining that two or more rules of aspecified kind match the source type of the input data stream, selectinga first rule in a lexicographic order of identifiers of the two or morerules.
 19. A computing system, comprising: a memory; and one or moreprocessing devices coupled to the memory, the one or more processingdevices to implement a pipeline stage of a data processing pipeline, thepipeline stage to: receive an input data stream comprising a pluralityof data points; determine a source type associated with the input datastream; select, among available rules, a rule having a source typeattribute matching the source type associated with the input datastream, wherein the rule specifies an action to be performed on a datapoint responsive to successfully matching a specified pattern to thedata point; responsive to identifying, among the plurality of datapoints, one or more data points matching the pattern, generate an outputof the pipeline stage by performing, on the one or more data points, theaction specified by the rule; and feed the output of the pipeline stageto a next pipeline stage of the data processing pipeline. 20.Non-transitory computer-readable storage medium comprisingcomputer-executable instructions that, when executed by a computingsystem, cause the computing system to: receive an input data streamcomprising raw machine data; process the raw machine data by a dataprocessing pipeline that produces transformed machine data, wherein thedata processing pipeline comprises an ordered plurality of pipelinestages, wherein a pipeline stage of the ordered plurality of pipelinestages applies a rule of a set of rules to an input of the pipelinestage, wherein the rule specifies an action to be performed on the inputof the pipeline stage responsive to evaluating a conditional expressionapplied to the input of the pipeline stage, wherein the action generatesan output of the pipeline stage, and wherein the rule is selected basedon a source type associated with the input data stream; and supply thetransformed machine data to a data collection, indexing, andvisualization system.